The imbalance between the supply and demand of shared bikes is prominent in many urban rail transit stations, which urgently requires an efficient vehicle deployment strategy. In this paper, we propose an integrated model to optimize the deployment of shared bikes around urban rail transit stations, incorporating a seasonal autoregressive integrated moving average with long short-term memory (SARIMA-LSTM) hybrid model that is used to predict the heterogeneous demand for shared bikes in space and time. The shared bike deployment strategy was formulated based on the actual deployment process and under the principle of cost minimization involving labor and transportation. The model is applied using the big data of shared bikes in Xicheng District, Beijing. Results show that the SARIMA-LSTM hybrid model has great advantages in predicting the demand for shared bikes. The proposed allocation strategy provides a new way to solve the imbalance challenge between the supply and demand of shared bikes and contributes to the development of a sustainable transportation system. Bike sharing has become a popular transport option in many cities because it is green and convenient and an essential feeder for other transportation modes such as buses and metro. In the emerging trend of mobility-as-a-service (MaaS) system deployment, which involves multimodal transportation, bike sharing may be promoted even further. As an important transport connector, shared bikes could increase the attractiveness of urban transit and thus potentially encourage transit use and contribute to sustainable transportation. Bicycle sharing complements and enhances public transportation . As maniShared bikes are traditionally dock-based because of the management and payment requirement and have also recently emerged as dockless. While the latter is considered more convenient because of the parking convenience, many problems remain, such as disordered parking, abandoned bikes, and illegal space occupation. Rail transit stations, as hot spots for shared bicycles, make this contradiction unprecedentedly acute. On the one hand, the excessive influx of shared bicycles is not conducive to the organization and evacuation of passengers. On the other hand, insufficient bicycle storage will lead to passengers being forced to choose other ways to complete their journeys, reducing passenger comfort and leading to a decrease in traffic accessibility . Both stMany studies have addressed the issues related to shared bikes from different perspectives, which provides a theoretical basis for the better planning of shared bikes around urban rail transit stations. Five main aspects were discussed in the literature, namely, the factors affecting the use of shared bikes , the chaThe prediction of demand for shared bikes is essential for planning bike-sharing deployment, because the optimal allocation strategy can only be obtained when the predicted bike-sharing demand is close to the actual deployment needs. Existing research has developed different models by focusing on either the demand prediction or allocation strategy using actual data. There is a lack of integrated models which combine the demand prediction and bike allocation. We argue that the separate models may be more useful for a specific purpose. In contrast, models integrating the two are more useful to effectively realize the balance between supply and demand and have more practical significance.In this study, therefore, we propose an improved bike-sharing allocation strategy to optimize the bike allocation around urban rail transit stations. A hybrid model based on time series and deep learning prediction theory is proposed, combining a seasonal autoregressive integrated moving average (SARIMA) model and a long short-term memory (LSTM) model to predict the usage of shared bikes around urban rail transit stations. Based on production and sales balance theory, a model to optimize bike allocation while minimizing the total dispatch cost is developed. Using 3-month big data of shared bikes in Beijing, the dynamic deployment strategy around urban rail transit stations is obtained by combining the actual dispatch process for shared bikes to meet the demand for shared bicycles during peak periods.The remainder of this paper is organized as follows: Section The purpose of the literature review is twofold: to discuss the relevant work on demand prediction and the allocation of shared bikes. These two aspects of research have been primarily conducted separately in the existing literature. The purpose of this review is not to offer a systematic review of the research on shared bikes but rather to present the content-wise research and the methods related to the two perspectives.The bike-sharing demand prediction has been discussed in different spatial scales, namely the city level, station clustering level, and single station level. City-level forecasting aims to predict coarser-grained bike usage at all stations throughout the city. Borgnat et al. , based oIn the case of the station-based prediction models, Sohrabi et al.  proposedAmong the studies on demand prediction, time-series regression models have most typically been applied. Kaltenbrunner et al.  used theIn addition, scholars have increasingly begun to combine different types of methods to predict the demand for shared bicycles. Mehdizadeh et al.  used a hBased on the predicted demand for shared bikes at different locations, optimal bike deployment has been an important subject for operators to balance the demand and supply in space and time. Developing an effective vehicle deployment planning model has been discussed extensively by scholars. For instance, Forma et al.  proposedReviewing the literature on the allocation strategy of shared bikes shows that most of the existing allocation schemes are based on simple manual allocation, and the allocation schemes do not cater to the hotspot usage areas of shared bicycles from a station perspective. In other words, they did not consider the demand for shared bikes from stations at different times, resulting in more significant problems in the management and operation of shared bikes in the surrounding rail transit stations. On the other hand, the existing prediction models have been developed independently, and their contribution to the optimal allocation of shared bikes has not been examined. Given this shortcoming, we propose an optimal shared bike allocation model incorporating the demand heterogeneity of shared bikes. More specifically, we propose a hybrid model to predict the demand for shared bikes by giving full play to the advantages of SARIMA and LSTM models and a deployment model to meet station-wide bike-sharing demand under the premise of cost minimization.The bike-sharing order data has an obvious time pattern, and its generation and ending have clear time points, so the time-by-time order data derived from the statistics belong to the classical time-series data. Classical models in time-series research include ARMA, ARIMA, and SARIMA. These regression-based models can effectively deal with the linear part of time-series data but are not sensitive to the nonlinear characteristics.In order to overcome the disadvantage of linear models in prediction with nonlinear features, recent studies have increasingly applied machine learning methods that are considered to effectively deal with the nonlinear association in the data, including traditional neural networks, deep learning, models such as recurrent neural network and LSTM, to enhance the model predictability. However, these machine learning methods depend highly on data and model selection. The results obtained also lack a certain degree of interpretability. Thus, recent activities regarding the synergy between theory-driven and data-driven methods have emerged .In this study, we contend that the time-dependencies in the time-series data can be best captured using the time-series data-based models, while the residual (random component) in the linear model may be best captured with a highly nonlinear structure in deep learning. In other words, we propose a model that integrates SARIMA and LSTM models, a SARIMA-LSTM hybrid model, such that the ridership of shared bikes can be predicted using advanced machine learning models while simultaneously maintaining the statistical features of the time-series data. In the following sections, we will first present the prediction models such as the SARIMA and LSTM models and focus on the hybrid version of the two models. Because the prediction model generates input data to optimize bike deployment, we also present the optimization model and explain how the two models are integrated.SARIMA is an autoregressive model for seasonal sequences developed based on the ARIMA model. By decomposing the time series into a regular time series and seasonal cycles, the model can eliminate the interference of periodic changes and improve its prediction accuracy. The model is generally denoted as mentclass2pt{minim in Eqs. , which \ in Eqs. , where \d and D. Next, the model’ parameters are estimated and determined, and the residuals of the model are tested. Finally, numerical predictions are made by the eligible The process of implementing a SARIMA model is depicted in Fig. LSTM is used to solve the long-term dependency and gradient problems, and its basic unit is shown in Fig. According to Fig. First, the forget gate is passed, and the forget gate operation expression is shown in Eq. , where \In the input gate, the state information of the candidate cell Combining with the cell state at the previous moment Finally, the value of the output gate from Eq. , and thewith Eq. . By procWf, Wc, Wi, Wz are the weight matrixes, bf, bc, bi, bz are bias vectors. These are exactly the eight sets of parameters that machine learning is designed to learn to obtain better predictions.In the above equations, The SARIMA-LSTM hybrid model combines the time-series prediction model and the neural network prediction model to predict the time-series volume of shared bikes,mentclasspt{minimaExtract linear features of the data.The data set was divided into a training set and a test set, which were built with a SARIMA model using SPSS. Then the linear fitting value Because the hybrid model involves parameter estimation of SARIMA and the training of LSTM, a joint model estimation process is needed. The structure diagram of the hybrid model is shown in Fig. (2) Analyze the nonlinear characteristics of data.After SARIMA modeling and prediction, the residual sequence reflecting the nonlinear structure of the data can be obtained, as shown in Eq. .13\documThen, the Keras deep learning library in Python and the TensorFlow package are used to build the LSTM network model. The residual sequence is used as the input value for training. After the parameters are stabilized, the model is used to predict the nonlinear value (3) The predicted value of future period To assess the predictive performance of the models, mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) are adopted. MAE and RMSE reflect the absolute error in the predicted and actual values, while MAPE reflects the relative percentage error. Smaller values of MAE and RMSE indicate a smaller fitting deviation of the model, thus better accuracy in prediction. In MAPE cases, a value lower than 10% has been considered empirically to be satisfactory. The formulas of the three indicators are shown below:By physically moving the bikes from one location to another, the demand in target areas can be matched, while the over-allocation in original areas can be released. Before redeployment begins, a redeployment plan will be developed to achieve a better balance. The plan usually includes transportation volumes and routes, with transportation costs being the essential basis for its development.After the morning peak, the enterprise allocation center calculates the inflow and outflow of shared bikes at various rail transit stations and deployment sites based on the order data uploaded to the system.According to the combined prediction model, input the inflow and outflow of shared bikes in the morning peak hours, predict the inflow and outflow of shared bikes in the peak hours and evening peak hours of all stations, and determine the export and import volume of the target area around the urban rail transit station and the upper limit of the number of shared bikes that can be accommodated and supplemented by the allocation station.Aiming at the company's minimum deployment cost, establish a shared bike deployment model based on the transportation problem of production and sales balance, and solve a specific shared bike allocation plan.The deployment of bike sharing is a system project. Preparing a deployment plan based on order data also requires the full cooperation of deployment command points, deployment locations, and deployment experts. In order to standardize the operational process of deployment, we summarize the management methods of different companies deploying shared bikes and propose the following deployment strategies.The allocation center sends the allocation plan to the specialists around each site. The allocation specialists complete the allocation of shared bikes in the allocation skylight according to the allocation instructions and report them in time.Analysis of the allocation strategy shows that the division of bike-sharing allocation areas is the basis for allocating work. In other words, we need to know which station needs to move bikes out and which one needs to replenish before we can optimize.As there are no fixed stations for bike sharing, users can rely on intelligent location devices such as onboard GPS to park and pick up their bikes anytime. That poses a challenge for deployment. The shared bike drop-off area is divided into different deployment units to simplify the deployment of shared bikes. In the actual scheduling process, the deployment center often determines the areas to be assigned based on historical experience, big data, analysis and dispatcher's instructions—in other words, without long transport distances for cost savings in the deployment. According to the analysis of shared bike data, 77.4% of shared bikes are manually assigned a straight-line distance of 3 km or less. Therefore, the straight-line distance between stations within the setup dispatch unit does not exceed 3 km.Based on the deployment units, the deployment sites of shared bicycles can be further divided. The areas around the urban rail transit station were considered, in general, a hot spot for the use of shared bikes; another study showed that shared bikes going to or leaving a station were distributed within 2 km of the station .k-means clustering algorithm to identify hot spots of bike-sharing usage. A 500-meter radius of the cluster center was used as the deployment site for the shared bikes.To disperse or supplement shared bicycles around stations, it is necessary to set up additional deployment sites. We filtered the latitude and longitude at the starting and ending points of bike-sharing orders within 2 km of all stations. We used a Here, we analyzed the number of shared bikes used around stations Fig. . Fig. 4UFigure The allocation demand in each area is calculated according to the flow difference, which refers to the difference between the inflow and outflow of regional shared bikes in the same period. The deployment requirement for each place can be calculated by Eq. .18\documThe inflow and outflow of shared bikes are predicted according to the SARIMA-LSTM combination model. Then, we calculated the allocation demand in the stations and the allocation site areas, respectively. For the urban rail transit stations, when For the deployment sites, when By physically moving the bikes from one location to another, the demand in target areas can be matched while the over-allocation in original areas can be released. Before redeployment begins, a redeployment plan will be developed to achieve a better balance. The plan usually includes transportation volumes and routes, with transportation costs being the essential basis for its development. The allocation task is to allocate the shared bikes to be transferred from all export urban rail stations to import urban rail stations and accommodation deployment sites. At the same time, the shared bikes required by all import urban rail stations can be transferred in from export urban rail stations and supplementary deployment sites. The critical point is that we need to minimize the costs incurred in shipping. Therefore, based on the balanced transportation model, we allocate shared bikes with the minimized cost as the objective function, as shown in Eq. .19\documThe definitions of the parameters in the formula are shown in Table The effect of constraints ① and ③ is to transport the excess bikes to the stations that need resupply. Constraint conditions ② and ④ mean that the number of shared bikes allocated at the supplementary and accommodation allocation stations should not exceed its upper limit. The last two constraints mean that the allocation of shared bikes cannot occur between the allocation stations.The costs incurred for deployment in this paper consist of labor and transportation costs, which are calculated in Eqs.  and 23. The model can be solved by the table on the operating method. We implemented such an optimization algorithm via MATLAB, in which the initial feasible solution uses the Vogel method, and the optimal solution is determined by closed-loop adjustment.In this section, we introduce the empirical analysis conducted based on the data in Beijing, the capital of China. First, we describe the data and fields being analyzed. Then the effectiveness of the hybrid model is verified based on the data. Taking the Xicheng District of Beijing as the basic dispatching unit, the allocation process for shared bikes around urban rail transit stations within the dispatching unit is elaborated in detail, and the deployment scheme for shared bikes around urban rail transit stations in the Xicheng District is formulated to verify the applicability of the proposed model.k-means was used to filter the possible deployment sites in the Xicheng District. According to the silhouette coefficient and within-groups sum-of-squares errors (SSE), the clustering results show the best performance when k = 932. The results show that there are 22 known urban rail transit stations and 28 qualified allocation sites were selected for deployment.The Xicheng District is located in the west of the central city area. Some stations have a higher number of shared bikes than other areas during morning and evening peak hours, which is more likely to have the imbalance problem between supply and demand of shared bikes. The Records having empty values in crucial fields such as order time, latitude, and longitudeAbnormal data repeatedly uploaded to the system in the same orderAbnormal data where the end time is earlier than the start time and multiple bikes are parked at the same locationData on starting or ending locations (longitude and latitude) outside the administrative geographical scope of BeijingAbnormal data with usage time less than 2 minutes or longer than 3 hours and driving distance less than 150 m or more than 10 kmThe primary data set used in this study is the payment transaction records of Mobike in Beijing from April to June 2018. Mobike is one of the first bike-sharing enterprises to enter the Chinese market and has recorded a huge number of rental trips over the years. Through vehicle smart locks and GPS positioning devices embedded in the bikes, location data are recorded as well as the registration data of users, including user ID, bike ID, and latitude and longitude information of start and end locations. Mobike generates about 900,000 orders daily, and some of the order data contain abnormal data due to various disturbing situations, such as smart lock failure, unstable GPS signal, and human error. These data will interfere with the accuracy of model results. Thus, before the analysis, a data cleaning process is implemented to screen out standard order data. Abnormal data in this study are identified based on the following five criteria:In addition to Mobike data, we use the data for Beijing rail transit stations, road networks, and administrative areas. These data are downloaded from OpenStreetMap. We use ArcGIS software to calculate the inflow and outflow of shared bikes around the Xicheng District. Moreover, we also call the Baidu Maps API to generate the shortest path data, which is used for the cost calculation of the optimization model.To verify the effectiveness of the hybrid model, we take the surroundings of Fuchengmen station as an example and use SARIMA, LSTM, and SARIMA-LSTM models for prediction. There were 35 valid data sets on orders for shared bikes around the Fuchengmen subway station. The time interval for the analysis of each data set was from 6:00 to 21:00 with a granularity of 1 hour. In this paper, the first 34 sets of data were used as the training set for the prediction model, and the last set of data was predicted.The training set data were first analyzed using the SARIMA model. Based on the results of the data processed by SARIMA modeling, when the model performance was optimal and the error was minimal, the parameter of An LSTM model was then used to perform the same prediction work. The LSTM network model framework in this paper was built using the Keras deep learning library in Python and the TensorFlow package. When using the LSTM model, a set of hyperparameters (as listed in Table ed Table . Table 3Finally, we ran the SARIMA-LSTM hybrid model with the same parameters as the separate predictions. The prediction results of the three models are summarized in Fig. Table With the forecast data, an effective and dynamic deployment plan can be developed in advance to meet the demand of residents for bike-sharing usage. Because of the low demand for shared bikes at some sites, when the demand at a site is less than half the maximum capacity of a dispatched bike, i.e., 30 bikes, it is considered that the site will not participate in the deployment. After the statistical screening, the Xicheng District participated in the deployment of 17 urban rail transit stations, including 11 exit stations with 1817 bikes and six import stations with a total of 738 bikes. These stations and the demand for shared bikes are plotted using GIS, as shown in Fig. Then the planning was done according to the bike-sharing allocation model. With a total of 3329 bikes to be shipped out and total demand of 2212 bikes, it is necessary to add a virtual allocation center as the 13th accommodation allocation station to accommodate an extra 1117 bikes. Based on the above consideration, the unknown quantities in Eq.  were detA more visible representation of the deployment plan is shown in Figs. Furthermore, a sensitivity analysis is conducted for the labor cost. The result indicates that the current solution is still optimal when As a sustainable transportation mode with the apparent features of green, flexible, and easy access, bike sharing can be used to connect urban rail transit, which contributes to solving not only the first and last mile problems but also transportation decarbonization as a whole. However, the imbalance between the supply and demand of shared bikes in space and time has remained prominent in many cities, resulting in various social problems. For both planners and the convenience of users, an efficient vehicle deployment strategy based on accurate demand prediction is seen as essential to ensure the sustainable benefits of shared bikes in urban transportation systems.In line with this concern, in this paper, we proposed a hybrid model which focuses on the dynamic deployment of bike sharing around urban rail transit stations based on a machine learning-enabled time-series prediction model. The purpose is to achieve the best deployment plan which minimizes the total cost by balancing the supply and predicted demand at different stations. The proposed hybrid model incorporates the advantages of both SARIMA and LSTM to predict the use of shared bikes.Using the actual data for the Xicheng District of Beijing, we compared the predicted values of a single model and the hybrid model. Results show that the hybrid model has higher prediction efficiency. This enriches the method for demand prediction of shared bikes. Apart from that, the bike-sharing deployment model around urban rail transit stations was developed to minimize transportation costs based on forecasting data. The resulting bike deployment plan would advise on the bike-sharing imbalance around urban rail transit stations.This study was conducted by setting the hot spot within 500 m of a station. However, as a flexible transportation mode, the bike-sharing system has many types of hot spots in the city. Therefore, the hot areas for using different types of shared bikes may also be combined to conduct further research on different travel structures and the global deployment optimization of shared bikes. In addition, as this paper focuses on meeting the demand for peak bike-sharing usage, the temporal granularity of the prediction and the establishment of the allocation model were simplified based on the actual deployment of the operating companies. Further research can be conducted to cover dynamic requirements on a time-by-time basis.


Capnocytophaga canimorsus that is part of the normal oral microbiome of pets. The clinical and pathological findings and the current and future therapeutic options are reviewed and discussed.The Waterhouse-Friderichsen syndrome represents a critical condition characterized by a septic shock associated with a disseminated intravascular coagulation causing the plugging of the microvascular network virtually all organs and systems, including the skin, the kidneys, the liver, and adrenal glands; the mortality rate is elevated, and survivors often must undergo multiple limb amputations. Here, we describe the uncommon case of an asplenic patient who developed this syndrome after a superficial wound caused by a dog bite causing an initial infection due to  The clinical trajectories of septic shock are extremely variable, ranging from cases promptly responding to the therapy to fulminant forms rapidly evolving toward a multiple organ dysfunction syndrome (MODS) and death. Recently, Kyriazopoulou et al.  identifiCapnocytophaga canimorsus, which is part of the normal oral microbiome in as many as 40% of cats and dogs [The Waterhouse-Friderichsen syndrome, which is an uncommon and often fatal kind of septic shock characterized by the bilateral adrenal hemorrhage, fulfills the diagnostic criteria of MALS due to its stormy clinical course and the involvement of all organs and systems; although it has been initially associated with Neisseria infections, several other bacterial and viral strains can cause it ; among tand dogs , appearsand dogs –8.Here, we describe the case of a patient who developed a MALS and a Waterhouse-Friderichsen syndrome following a minor wound caused by a dog bite.A 53-year-old woman was bitten on her right wrist by a dog. The remote history revealed a splenectomy due to trauma 23 years prior the current admission. As the wound appeared superficial, she seeked medical attention at the local emergency department (ED) only 3 days later when the site became reddish and painful; after the cleansing of the wound, she was discharged home with a prescription of an oral macrolide three times/day. On the morning of the 2nd after this visit, her husband took her to the hospital again as she was confused and many petechiae appeared on her trunk and upper limbs; after a short stay in the ED, she was admitted to our intensive care unit (ICU) with the diagnosis of septic shock associated with a DIC.At the clinical examination, the patient was hypotensive (80/50 mmHg) and drowsy but still arousable; the torso and the upper limbs presented multiple hemorrhagic petechiae that in the following day extended to the whole body, evolving in blisters that subsequently exfoliated Fig. . The patC. canimorsus can determinate a Waterhouse-Friderichsen syndrome evolving in a MODS [In asplenic and/or immunodepressed patients, the infection caused by n a MODS . The clin a MODS –14. The n a MODS .C. canimorsus infection due to the asplenia, the recent dog bite, the stormy course, and the gram bacilli visible inside the neutrophils that were identical to those described in other cases of septic shock due to same organism [In our patient, although the cultures remained negative after 3 weeks of incubation, the MALS can be likely ascribed to a organism : thus, iorganism .C. canimorsus-related septic shock, the MODS was determined by the highly prothrombotic DIC; although the trigger(s) are not precisely identified due to the unusual occurrence of this infection, it is has been hypothesized that the pathogen adhesion to the endothelium causes the upregulation of adhesion molecules and the margination of polimorphonucleates whose proteases impair the endothelial thrombomodulin that, in turn, substantially decreases the function of the proteins C (PC). This is the same mechanism occurring in patients with meningococcal PF [Actually, in ours as in other cases of occal PF , 17.C. canimorsus, some considerations for the treatment are warranted.AS different circumstances account for the poor outcome occurred in ours as well in other patients with MALS due to C. canimorsus should be encouraged to seek immediate medical advice in the event of a pet bite.First, our patient presented a full-blown MODS already at the admission; then, since most of the treatments indicated in the Surviving Sepsis Campaign are time dependent, they could hardly modify the clinical course in an advanced phase of the disease. Consequently, patients with known risk factors for septic shock due to C. canimorsus-associated MALS can be ascribed more to the occurrence of DIC than to the infection by itself; consequently, measures aiming to restore the anticoagulant capabilities, including the administration of FFP and AT III, should be adopted; the administration of PC zymogen concentrates could be considered, but the reduced or absent levels of thrombomodulin might prevent its activation.Second, the high mortality of Third, the extremely elevated CK despite the presence of peripheral pulses values likely indicates a diffuse muscle necrosis due to the DIC-associated plugging of the microvascular network despite the presence of peripheral pulses  it is arC. canimorsus septic shock fail to respond to the current treatments, other approaches are required to abate the hyperinflammatory reaction that is the primer of the DIC; recently, Shakoory et al. [Finally, as it appears that most patients with y et al.  reviewedy et al. . Then, tIn our patient, all the therapeutic options that included both current and adjunctive treatment of septic shock failed to influence the clinical course likely due to the advanced phase of the disease. Patients with known risk factors for C. canimorsus infection and subsequent complications should not be discharged home but admitted to the hospital for a closer clinical survey and a prompt referral to the ICU.


Serious games for the training of prevention behaviors have been widely recognized as potentially valuable tools for adolescents and young adults across a variety of risk behaviors. However, the role of agency as a distinguishing factor from traditional health interventions has seldom been isolated and grounded in the persuasive health communication theory. Fear appeals have different effects on intentions to perform prevention behaviors depending on the immediacy of the consequences. Looking into how to increase self-efficacy beliefs for health behavior with distant consequences is the first step toward improving game-based interventions for adverse health outcomes.This study aimed to investigate the effect of agency on self-efficacy and the intention to drink less alcohol in an interactive digital narrative fear appeal. Furthermore, the communicated immediacy of threat outcomes was evaluated as a potential moderator of the effect of agency on self-efficacy.A web-based experimental study was conducted with university students (N=178). The participants were presented with a fear appeal outlining the consequences of excessive alcohol use in a fully automated web-based interactive narrative. Participants either had perceived control over the outcome of the narrative scenario (high agency) or no control over the outcome (low agency). The threat was either framed as a short-term (high immediacy) or long-term (low immediacy) negative health outcome resulting from the execution of the risk behavior .A total of 123 valid cases were analyzed. Self-efficacy and intention to limit alcohol intake were not influenced by the agency manipulation. Self-efficacy was shown to be a significant predictor of behavioral intention. The immediacy of the threat did not moderate the relationship between agency and self-efficacy.Although agency manipulation was successful, we could not find evidence of an effect of agency or threat immediacy on self-efficacy. The implications for different operationalizations of different agency concepts, as well as the malleability of self-efficacy beliefs for long-term threats, are discussed. The use of repeated versus single interventions and different threat types  should be tested empirically to establish a way forward for diversifying intervention approaches.ClinicalTrials.gov NCT05321238; https://www.clinicaltrials.gov/ct2/show/NCT05321238 Excessive alcohol abuse is an alarmingly large cause of death in European Union member states, as well as in Norway, Switzerland, and the United Kingdom . This isWe argue that the use of interactive digital narratives (IDNs) could be a promising tool to enable the training of preventive behavior through direct interaction with the narrative as a protagonist. In contrast to traditional passive narratives, an IDN involves computer-based interactive storytelling that allows the user to intentionally influence a nonlinear narrative. This means that the interactor is not only experiencing fictional reality but can also be part of it by being able to make meaningful decisions that affect narrative outcomes . What seprevention behavior is used to describe the behavior needed to avert a health threat. For example, in the case of messages promoting vaccinations to prevent an infection with a disease, receiving the vaccination constitutes prevention behavior.Fear appeals have been widely assessed and used by many health communication researchers for interventions that focus on behavioral changes. Fear appeal communication aims to stimulate behavior change through the provision of a threat that causes a fear response. The message receiver is assumed to be motivated to resolve the arousal caused by the fear response and is presented with a solution to avert the threat. If the appeal is successful, the message perceiver adopts the prevention behavior. The term Fear appeals are commonly used in many public health campaigns, making use of mostly text-based or pictorial stimuli to elicit fear responses. One of the most widely applied, as well as well-researched, theories on the underlying process of fear appeal communication is the extended parallel process model (EPPM) . The corself-efficacy means that a person needs to believe that he or she has the ability to execute a response that is recommended to him or her in the context of a fear appeal . According to the EPPM, perceived efficacy (self- and response efficacy) communicated in a fear appeal determines whether a fear message is accepted. It is important to note that the evidence for the abovementioned effects does not rely solely on studies dealing with the health threat of alcohol exclusively but rather encompasses studies across a variety of risk behaviors such as smoking, drug use, and risky driving. Previous work supports the notion that fear appeals, specifically those addressing alcohol use among young adults, can raise efficacy and affect intention to drink alcohol (see the studies of Stainback and Rogers [Witte  posited d Rogers  and Moscd Rogers ); howevestory world describes the structure surrounding the events of the story, which includes the locations and persons relevant to the events taking place [Fear appeals used in public campaigns often make use of simple narratives through pictures and textual information that are aimed at persuading the message receiver by providing a way of averting the threat . These simple narratives  heighten the relevant affective responses for persuasion , leading to more persuasive fear appeals . Althougng place . Transpong place .causality effect to interactive game-like formats. Although a passive narrative appeal can affect character intentions to enhance persuasion, an interactive appeal could potentially increase this effect by making the message receiver an active participant in the behavior addressed by the appeal. Earlier research clearly points to evidence supporting the effectiveness of narrative appeals, but there is little work on the potential influence of providing the message receiver with agency in interactive narrative appeals that strengthen the causal connection between prevention behavior and threat aversion.In their review of narrative interventions, De Graaf et al  concludeagency has been widely used for different forms of interaction with games and IDNs. In IDN research, agency is most commonly conceptualized as the perceived meaningfulness of action with regard to narrative outcomes. As argued by Murray [competence in how their actions affect the story world, and this need can be fulfilled through the provision of agency. This competence in learning that player actions have consequences  on the story world is a necessity for meaningful interaction . For example, the ingestion of certain foods within the game world results in an increase in the (visible) weight of the main character.The term y Murray  and othey Murray . Accordiy Murray , agency y Murray . They deMany investigations have provided interactive elements in their message design .Real-life self-efficacy beliefs are amenable through experimental manipulation in persuasive communication ; howeverIn line with this, in this study, we expect that self-efficacy beliefs about real-life prevention behaviors can be affected by the provision of agency in an interactive narrative fear appeal. Although it is only a stepping stone in examining the process of persuasive IDNs for behavior change, the role of interaction as a key differentiator in the experience between passive and active media has rarely been investigated in a highly controlled narrative environment.low immediacy), interventions informing about immediate negative outcomes (high immediacy), such as bad breath, have been shown to be more effective in evoking behavior change [Although fear appeals for immediate threats have been shown to be effective, there is less evidence showing EPPM’s effectiveness for temporally distant threats . For exar change . This poefficacy from successfully conducting actions. The immediacy of a response causes temporal congruency between the cause and effect between player actions and game events. In the case of a cause-effect chain with high immediacy, there is little question on the part of the player with regard to their agency and, therefore, their self-efficacy to affect the story world. In terms of integrating a distant threat into an IDN, the narrative format allows the author to advance time in the story world, which opens up the opportunity to present players with an immediate causal connection between executed behavior and long-term consequences. Using this temporal flexibility of a narrative format, we hypothesized that the effect of agency on self-efficacy is moderated by the framing of the threat as either short-term (high immediacy) or long-term (low immediacy) adverse health outcomes.As discussed by Klimmt and Hartmann , playersThis study aimed to assess the effect of agency and threat immediacy on self-efficacy and, consequently, the intention to perform prevention behaviors. The following hypotheses were tested using an IDN in this study:Hypothesis 1: Higher agency in the narrative progression of the IDN fear appeal results in higher perceived self-efficacy for prevention behavior.Hypothesis 2: The effect of agency on self-efficacy is moderated by framing the threat as an adverse health outcome with either high or low immediacy.Hypothesis 3: Higher perceived self-efficacy will lead to a higher intention to perform the target behavior.Data were collected from November 26 to December 18, 2020. The hypotheses, study design, and planned analyses were preregistered using the preregistration platform provided by the Wharton Credibility Lab. The independent variables were agency (low or high) and threat immediacy (low or high). Self-efficacy and behavioral intention served as the dependent variables in this study.This study was approved by the Research Ethics and Data Management Committee (REDC) of the Tilburg School of Humanities and Digital Sciences (TSHD) and was conducted as a 2×2 between-subjects experimental study (reference number: REDC 2020.141).A total of 178 participants were recruited from the Human Subject Pool of Tilburg University. All participants were enrolled in a master’s or bachelor’s degree program and received 0.5 course credits for participation in the study. Participants were allocated equally across all 4 conditions, and the study was conducted entirely on the web. No personally identifiable data (including IP addresses) were collected to ensure the anonymity of all participants.Data were collected in 2 phases. The first half of the participants  who enrolled in the study were allocated to the high-agency condition. After the completion of data collection for high-agency participants, the second half  of the participants were assigned to the low-agency condition. The low-agency narratives were matched in terms of narrative content to the narratives of the high-agency group who took part in the study before them. All participants were randomly assigned to either the high- or low-immediacy conditions. Given the short time frame of data collection and the absence of events that could have an influence on drinking-related behavior , we assumed the time gap between the participation of the first and second halves of the participants to not affect our measures. Sufficient computer literacy and English language capabilities were assumed among the student population used in this study.drink an alcoholic drink for all decisions in the interactive narrative, and (6) did not show signs of alcohol abuse. Participants were excluded if they were aged <18 years  or completed the questionnaire in an unreasonably short amount of time . In addition, participants were excluded if they failed 1 of the 2 attention checks, indicating that the narrative was not read attentively , or they failed the manipulation check asking them to recall whether they were presented with short- or long-term (high or low immediacy) consequences of excessive drinking . All participants who chose an alcoholic drink for all decision points in the narrative were excluded . Choosing to drink an alcoholic drink for all presented decisions would lead to the participants not executing the prevention behavior ; therefore, they were also not presented with a self-efficacy statement. This means that participants would not be exposed to a full fear appeal, as they only received a severity, susceptibility, and response efficacy statement. As elaborated earlier, according to the EPPM, without self-efficacy, a fear appeal is unlikely to lead to the adoption of the proposed prevention behavior. Hence, 1.7% (3/178) of participants had to be excluded as they chose to drink alcoholic drinks for all the presented decisions. Finally, 3.9% (7/178) of participants were excluded because of signs of alcohol dependence as they scored >15 on the Brief Young Adult Alcohol Consequences Questionnaire (B-YAACQ) [Participants were included in the analysis if they (1) were aged >18 years, (2) spent >480 seconds completing the survey, (3) passed both attention checks relating to narrative content, (4) correctly identified their experimental condition in the manipulation check, (5) did not choose to B-YAACQ) . As the The final sample used for analysis comprised 123 participants, with 59 (48%) in the high-immediacy condition and 64 (52%) in the low-immediacy condition. The split between high and low agency was almost equal to 49.6% (61/123) of participants in the high-agency group and 50.4% (62/123) of participants in the low-agency group.The sample was representative of a student sample with a mean age of 21.4 (SD 3.086) years. Most held either a high school  or bachelor’s diploma , with few  participants having completed their master’s degree. There was a large gender imbalance in the sample, with approximately 72% (88/123) of the sample being female.imagine being in the place of the protagonist for the entirety of the narrative. On the basis of their condition, they were presented with 1 of the 4 IDNs. After completion of the narrative, participants were asked to fill out several questionnaires. First, the 2 dependent measures—self-efficacy and behavioral intention—and the perceived fear measure were presented. Next, the participants had to fill in the attention check by answering 3 questions concerning the content of the narrative. This was followed by manipulation checks. Following this, severity, susceptibility, response efficacy, and perceived agency were measured. Finally, questions regarding disinhibition, perceived control over drinking, and frequency of drinking too much alcohol, as well as the alcohol dependence measure, were presented. After completing the questionnaires, participants were debriefed and offered to watch a video to potentially restore any adverse effects resulting from the study.The entirety of this study took place on the web. Participants signed up through the Tilburg University Human Subject Pool. They were first presented with an information letter and then informed consent before agreeing to participate in the study. Next, demographic data  were collected, and participants were instructed to The narratives showed a fictional scenario in which the participant was presented with a fear appeal message, together with a pictorial fear stimulus . Pictures were added throughout the story to (1) improve transportation into the story world, which has been shown to improve narrative persuasion , and (2)The first part of the fear appeal message , left wablood_60) as part of the Nencki Affective Picture System [The scenario of a house party was chosen as it is a familiar example to our target population and should, therefore, be easy to imagine and connect to real-life risk situations (to enable behavior transfer). Choosing organ failure as a threat made it possible to keep the threat congruent across different immediacy conditions while only changing the time frame. The threat needed to be severe to cause sufficiently high levels of perceived fear, whereas the pictorial stimuli needed to be congruent with the threat without invoking disgust. Disgust has been shown to potentially undermine appeal content when combined with fear stimuli . The pice System . This pie System ). Accorde System , the effA total of 4 different versions of the narrative fear appeal were created (1 per condition).fold back to central events to enable the presentation of all participants with the same decision points. Once a participant declined the drink at any of the decision points, he or she was presented with a self-efficacy statement (plus SE) and was no longer able to choose options related to alcohol consumption. For example, a participant who accepted the alcoholic drink offer at the first decision point  or continuous consumption of large amounts of alcohol over a period of months (low immediacy). Similarly, the threat aversion message  indicated good reliability of the scale (Cronbach α=.882).Self-efficacy beliefs were measured by adapting the item stems from Shi and Smith  to fit tconsumed , the itestrongly disagree to strongly agree. The reliability analysis of the 3-item behavioral intention measure  indicated good reliability of the scale (Cronbach α=.882).Behavioral intention was measured using items adapted from Fisher et al . Althougnot at all to very much. The items asked participants about the intensity of affective response toward the message; for example, “How much did this message make you feel frightened?” Reliability analysis of the 6-item fear measure  indicated good reliability of the scale (Cronbach α=.898).Items for the fear measure were taken from the Witte  recommenItems for perceived susceptibility and severity were adapted from Shi and Smith . Althougstrongly disagree to strongly agree. As with the self-efficacy and behavioral intention measures, the measures are phrased in terms of either drinking too much or drinking less to account for individual differences in the perception of the appropriateness of alcohol intake.Severity, susceptibility, and response efficacy items were measured with 3 items each on 7-point Likert scales from The reliability analysis of severity , susceptibility , and response efficacy  showed moderate scale reliability.strongly disagree to strongly agree. Reliability analysis of the 5-item perceived agency measure  indicated good reliability of the scale (Cronbach α=.881).To validate agency manipulation, a scale was included to measure perceived agency after the participants experienced the narrative. The items for perceived agency were adapted from Fendt et al . A questdrinking too much frequently over a long period (low immediacy) or drinking too much at a party over a short period (high immediacy).To check whether participants perceived a difference in the immediacy of the presented threat, they were asked to recall the content of the threat message presented at the beginning of the narrative. Participants had to indicate whether the protagonist died because of disinhibition) has been shown to indicate that low sensation seekers exhibit stronger affective responses to fear appeals addressing alcohol use [strongly disagree to strongly agree.To account for potential individual differences between participants, disinhibition, drinking frequency, alcohol dependence, and perceived control over drinking behavior were measured. Behavioral inhibition, measured by a subscale of the Sensation Seeking Scale-V by Zuckerman , has beeohol use  and are ohol use . The iteohol use  but alteThe subscales for measuring frequency and perceived control regarding excessive alcohol consumption were taken from Carrera et al . The itedrink alcohol in excess and was measured on a 7-point Likert scale ranging from never to very frequently. Perceived control was measured by 2 items asking participants whether they could control and stop drinking alcohol whenever they wanted. These 2 items were measured on 7-point Likert scales ranging from strongly disagree to strongly agree.Frequency was measured by asking participants how often they As alcohol dependence is difficult to measure accurately in student populations, the B-YAACQ was developed by Kahler et al  to measu1), moderation of the relationship between agency and self-efficacy by the outcome frame , effect of self-efficacy on behavioral intention , and potential direct effect of agency on behavioral intention (path c′). A total of 5000 bootstrap samples were used for the analysis.The conceptual model shown in f=0.25) with the desired power level of 0.8 for our 2×2 research design. To reach adequate power, 32 valid samples were needed per condition.The a priori power calculation assumed a medium effect size , inhibition , and age , indicating that the conditions did not have significantly different distributions of scores for alcohol intake, inhibition, and age. Chi-square tests were administered to ascertain the potential differences in the distribution of drinking frequency and gender across conditions. For drinking frequency, participants were grouped as nondrinkers, rare drinkers, or occasional and frequent drinkers. There were no significant differences in distributions across conditions for gender  or drinking frequency . In summary, no significant differences in perceived control over drinking behavior, inhibition, drinking frequency, age, and gender were found between the conditions. Thus, the randomization was successful.Three 1-way ANOVAs were conducted to test whether group assignment had an effect on the perceived control over alcohol intake, inhibition, or age. We obtained no significant results for group membership on perceived control (t test (2-tailed) was performed to check for the effectiveness of the agency manipulation by comparing the condition (low or high agency) with the perceived agency measures. There was a significant difference between the low-agency  and high-agency  conditions , indicating an effective manipulation of agency.An independent-sample 1 indicates that the different levels of agency (low or high) are not significant predictors of the self-efficacy measure , indicating that we had found no evidence to support hypothesis 1. There was also no interaction effect between agency and outcome frame as a predictor of self-efficacy . This indicates that the relationship between agency (low or high) and self-efficacy was not affected by the difference in outcome frames (high immediacy and low immediacy) presented to the participants (hypothesis 2). Looking at path b, we see that self-efficacy is a significant predictor of behavioral intention . Given that self-efficacy is the mediator in this model, agency did not have a direct effect on behavioral intention . Therefore, we observed no evidence to assume that the manipulation of agency and threat immediacy affected behavioral intention measures. An overview of these effects can be found in The analysis of path at73=1.928; P=.06). Similarly, we evaluated whether a possible ceiling effect of self-efficacy measures might have influenced the overall effect of agency on behavioral intention. After conducting the median split, it was found that only a small number of participants  scored below the median, with a relatively large proportion scoring exactly on the median . Given the low variability in the sample and, thus, only a limited sample, it was decided not to conduct any further analysis on the remaining 32.5% (40/123) of participants.Additional exploratory analyses were conducted to contextualize the null effects. A median split was conducted by analyzing the effect of agency on self-efficacy for participants scoring high on susceptibility to ascertain whether the appeal or the pre-existing characteristics of the sample caused a potential floor effect for the susceptibility measure. The adjusted sample  showed no significant relationship between the agency and self-efficacy measures  or long-term (low immediacy) adverse health outcome. A significant effect was found for the relationship between self-efficacy and behavioral intention. To point forward for future studies, the null findings of this study need to be discussed with regard to their contribution by contrasting the different approaches taken for this study.In this study, agency had no significant effect on the self-efficacy perceptions of the participants. Agency was operationalized as the one-time execution of an action with a meaningful impact on the outcome of the narrative. To achieve this, a foldback structure was used to ensure that participants would encounter the same narrative content and be presented with a complete fear appeal where the prevention behavior was executed, and a self-efficacy message could be displayed. Although the manipulation check showed a difference in perceived agency between high- and low-agency conditions, the perceived agency might still not have been impactful enough to cause a change in the self-efficacy measure. Owing to the nonexistence of related work on the effects of agency on self-efficacy, these results are difficult to compare with other operationalizations of agency in the field. However, looking at the different theoretical conceptualizations of agency, we can derive possible explanations.The manipulation in this study could conceivably be seen as a low-agency condition if agency is defined as the balance of affordances and constraints provided by the system . Using tHeightening perceived completeness could be achieved, given the successful use of illusory agency, presenting options that are perceived to be more complete even if they fold back to the same outcomes, or by loosening authorial control in more open experiences such as a sandbox game that enables emergent storytelling. The question is whether the elicitation of self-efficacy through agency is reliant on control over the chronology of message components or whether heightened agency through emergent storytelling can make up for the loss in control by providing a closer link between the protagonist and the message receiver in terms of perceived completeness of the available actions. Taking this a step further, there is evidence to form the assumption that agency does have an effect on persuasion, even if the agency manipulation is completely decoupled from the persuasion attempt and simply induced as a state before the intervention .The wide range of used agency definitions and their heterogeneous operationalization make generalizations about agency effects in health interventions difficult. For future studies, the plural modality of agency should be taken into account  and testIn this study, we found no significant effect of threat immediacy on the relationship between agency and self-efficacy. Organ damage was chosen as the threat as it can be a result of either short-term or long-term risk behavior and was henceforth communicated as a consequence of a single night or multiple months of drinking. There is ample work evaluating the effect of fear appeals on one-time versus repeated risk prevention behaviors, showing that appeals advising the one-time execution of behaviors are more likely to succeed . HoweverIn most IDN research, as well as game studies, behavior transfer between digital and real-life behavior is often assumed to be high when the digital execution matches that of real-life analog behavior. The prevention behavior for the threat in this study does not constitute a one-time behavior, as it needs to be executed multiple times for the threat to be averted. With regard to immediacy in this study, there is, therefore, a mismatch between both our short- and long-term threats and our digital narrative in which the prevention behavior (rejecting a drink) is only executed once. Although a single appeal message can influence self-efficacy for repeated prevention behaviors , in thiA possible direction for future studies would be to make the long-term, as well as the short-term, impact of risk behavior more salient through the provision of repeated interventions. As discussed by Shi and Smith , EPPM-baoptimism concerning the likelihood of adverse health effects has been well established in other studies and potentially creates reactance during persuasive attempts for risk behaviors involving drinking, drunk driving, and smoking in young adults [The null effects for agency and threat immediacy could also result from the low perceived susceptibility of the target population (young adults) to the threat used in this study (organ failure because of excessive drinking). The so-called g adults -50. OrgaThe issue of perceived threat susceptibility can be further explained by our exclusion criteria. The criteria specifically excluded participants who showed signs of alcohol dependence when they might actually have been participants for whom the threat would be most relevant and the preappeal efficacy would be low. However, as the aim is to increase the effectiveness of early intervention approaches, more effective ways of eliciting prevention behaviors for normal populations should still be investigated more thoroughly.An alternative approach could be the use of appeals that do not focus on health threats to increase susceptibility as a prerequisite for successful narrative persuasion. Health threats themselves have been the main focus of health-related interventions and are effective for older cohorts; however, this might not always be the case for younger populations. As suggested by Pechmann et al , the useThis study did not use a pre- or postmeasures design to account for the potential influence of baseline beliefs already present in participants before the appeal message was presented. However, it has to be noted that disinhibition, drinking frequency, alcohol dependence, and perceived control over drinking behavior were measured to account for individual differences between participants. These were measured after the appeal message so as to minimize the influence on the responses to the IDN appeal. Although we believe that these constructs relate to either stable traits or general behavior and therefore should not have been influenced by the manipulation, we cannot fully discount the potential influence of the IDNs on these constructs.Furthermore, it should be noted that the current sample was predominantly women . Previous studies have shown gender differences in the effectiveness of fear appeals, which might have influenced the results. In general, this sample might not be representative of the general population.Finally, this study fell short of its goal of collecting a total of 140 valid samples, leading to a potential shortcoming in statistical power, given the complex study design. Furthermore, it could even be that the authors assumed a medium effect size for a single intervention transferring virtual behaviors to real-world beliefs and behavioral intentions.Finally, the context of this study must be mentioned. The study was conducted during the COVID-19 pandemic, which limited the participants’ exposure to risk situations and could have skewed the general perception of the appeal as personally relevant. Although there was no active lockdown in place at the time of data collection, there was a limit to the number of people allowed to gather more generally. This made the otherwise common risk environments that facilitate drinking, such as parties, almost nonexistent. As social facilitation of drinking is one of the core determinants of risk behavior related to excessive alcohol use for college students , the conIn this preregistered study, no significant effects were found for agency on self-efficacy and behavioral intention. In addition, we observed no effect for the influence of threat immediacy on the relationship between agency and self-efficacy.The multitude of conceptual distinctions of different forms of agency and the different ways of integrating them into IDNs provides a challenge that needs further evaluation to affect self-efficacy and, more generally, behavioral change. There is a need to empirically test the difference between conceptually different agency concepts to establish their impact on persuasiveness in narrative health interventions. Although there is evidence to assume the effects of interaction on antecedents of narrative persuasion , more work is needed to understand how different agency conceptualizations directly affect the processing of health messages. In addition, more work is needed that empirically contrasts passive and interactive interventions to isolate the effects. Much of the previous work done in this field use fully-fledged serious game experiences without passive controls, making it difficult to ascribe effects to single factors in such complex systems. With regard to the theoretical assumptions underlying narrative fear appeals, the personal relevance of the content should be ensured through pilot testing before the deployment of the intervention. Without creating the personal relevance of the narrative fear appeal, the intended effects could be undermined because of a lack of perceived susceptibility to the threat. Understanding how different kinds of threats are perceived by different target populations should be investigated more thoroughly to adapt interventions more effectively. Furthermore, the effect of intervention repetition for different kinds of prevention behaviors is poorly understood, making generalizations across different fear appeal threats difficult. A systematic approach contrasting one-time and repeated prevention behaviors for different kinds of threats could establish a clearer picture of the potential necessity of adaptations for threats that are the result of repeated risk behaviors.In conclusion, despite the null findings for the effects of agency on self-efficacy, this study highlights the potential for further exploration of agency concepts and threat types in terms of their embedding into a narrative intervention.


Traditional Chinese medicine (TCM) prescriptions have made great contributions to the treatment of diseases and health preservation. To alleviate the shortage of TCM resources and improve the professionalism of automatically generated prescriptions, this paper deeply explores the connection between symptoms and herbs through deep learning technology, and realizes the automatic generation of TCM prescriptions. Particularly, this paper considers the significance of referring to similar underlying prescriptions as herbal candidates in the TCM prescribing process. Moreover, this paper incorporates the idea of referring to the potential guidance information of corresponding prescriptions when model extracts symptoms representations. To provide a reference for inexperienced young TCM doctors when they prescribe, this paper proposes a dual-branch guidance strategy combined with candidate attention model (DGSCAM) to automatically generate TCM prescriptions based on symptoms text. The format of the data used this paper is the “symptoms-prescription” data pair. The specific method is as follows. First, DGSCAM realizes the extraction of key information of prescription-guided symptoms through a dual-branch network. Then, herbal candidates in the form of prescriptions that can treat symptoms are proposed in view of the compatibility knowledge of TCM prescriptions. To our knowledge, this is the first attempt to use prescriptions as herbal candidates in the prescription generation process. We conduct extensive experiments on a mixed public and clinical dataset, achieving 37.39% precision, 25.04% recall, and 29.99% F1 score, with an average doctor score of 7.7 out of 10. The experimental results show that our proposed model is valid and can generate more specialized TCM prescriptions than the baseline models. The DGSCAM developed by us has broad application scenarios and greatly promotes the research on intelligent TCM prescribing. As a documented summary of the valuable experience of the ancient Chinese people in their struggle against disease , 2, tradFaced with these problems, there is an urgent need for an easy way to provide convenience for patients and inexperienced young doctors. With the rise of artificial intelligence , some peIn order to obtain prescriptions which are more in line with TCM prescribing concepts and achieve prescription “generation” rather than herbal “mapping”, more researches on prescription generation have focused on the use of seq2seq structures –24. WangTo address the above issues, this paper establishes new Chinese herbs guided and prescription candidates' attention depth model based on symptoms text. This model incorporates TCM background knowledge into the seq2seq structure to assist in prescription generation. The originality of our proposed method has two main components. On the one hand, this paper retains the original encoder structure for extracting critical symptoms information, and additionally uses prescription knowledge to build a branch network to guide the extraction of the symptoms information. This cross-conceptual knowledge source guidance method will be a worthwhile attempt. On the other hand, our method also incorporates the knowledge of TCM prescription compatibility. The concept of herbal preselection proposed by Li et al.  is expanWe propose a novel seq2seq-based Chinese herbs guided and prescription candidates attention depth model to generate TCM prescriptions. A dual-branch guidance strategy combined with candidate attention model (DGSCAM) is designed to analyze symptoms text information and generates prescriptions that are consistent with the concept of TCMFor all we know, our work is the first to investigate the effect of prescriptions as herbal candidates on prescription generation. We propose a dual-branch information extraction module (DIEM), so that prescriptions can be used to guide the extraction of symptoms information. At the same time, in order to give full play to the positive effect of TCM prescription compatibility rules on the automatic generation of TCM prescriptions, we also propose a task-qualified knowledge base matching module (TKMM)The feasibility of this method has been verified by experiments. Feeding symptoms text into our model generates symptomatic prescriptions with overall efficacy. The results show that our model has certain advantages over the currently popular prescription generation models. Meanwhile, the method has been recognized by professional TCM doctorsThe main contributions of this paper are as follows:Since many problems do not exist in isolation, they can all be solved through domain knowledge and multidimensional thinking , 28. GuiThe attention mechanism can automatically learn and calculate the contributing degree of the input data to the output data. And it can explore the regions that need to be focused in a focused manner and suppresses the interference of weakly relevant information. Bahdanau et al.  used an Cs from the symptoms S, this paper designs a dual-branch information extraction module (DIEM). The branch of the cross-concept knowledge source guidance module (CKGM) in DIEM can assist the EnGRU branch to extract Cs using the potential association of prescriptions with symptoms. Second, in the decoding part, in order to consider the compatibility knowledge of TCM prescriptions, this paper proposes a task-qualified knowledge base matching module (TKMM). TKMM calculates the matching of symptoms S with the symptoms in the knowledge base (KB) built in this paper, and obtains the base prescriptions to form the prescription-based herbal candidates pool (HCP). Meanwhile, we propose the multidimensional complementary attention module (MCAM). MCAM combines the attention of HCP with the attention of the conventional herbal library (CHL) to improve the quality of generated prescriptions. Moreover, we improve a new coverage mechanism (Cover) to solve the problem of duplicate herbs in prescriptions H generated by the candidates' coverage decoder (DeGPT2). We will further explain the four important components of the model in this paper: DIEM, TKMM, MCAM, and DeGPT2 in the following sections.An overview of the dual-branch guidance strategy combined with candidate attention model (DGSCAM) proposed in this paper is shown in In this work, we use two datasets: the “symptoms-prescription” data pairs set (SP) and the “prescription-efficacy” data pairs set (PE). (1) There are three sources of the “symptoms-prescription” data pair set (SP), namely the public data set (CKCEST) , the traS into Cs through the knowledge of TCM syndrome differentiation. Since there are multiple complex logical relationships among a group of symptoms, the expression of each symptom does not independently represent a certain function of the body. Therefore, when extracting Cs from symptoms S, it is necessary to comprehensively consider the relationship among the symptoms. To this end, this paper encodes the symptoms S using the bidirectional GRU  shown in Equation , set the epoch number to 500 and the batch size to 64. During the training process, the model with the lowest total loss is saved for validation on the test set.All experiments are conducted on a workstation with Ubuntu 18.04 LTS, Intel(R) Xeon(R) W-2102 CPU and NVIDIA TITAN XP GPU. This paper implemented GSCCAM based on PyTorch. Our model sets the embedding size of Chinese characters in symptoms and prescription markers to 512, and the hidden states size of bidirectional GRU and GPT2 to 512 as well. We use the Adam optimizer  in the test dataset, they are defined asH represents the prescription sequence generated by the model GSCCAM. Plabel represents the “prescription” in the “symptoms-prescription” data pair, that is, the label prescription sequence.In order to automatically evaluate the performance of our model, this paper selects precision, recall, and F1 score as automatic quantitative evaluation metrics, which are commonly used in studies on TCM prescription generation. For all “symptoms-prescription” data pairs  and herbal compatibility (HC) , both ofKDHR : a graphPTM : the topCoverage-Soft-Model : it is aHerb-Know : a seq2sTPGen : a seq2sThis paper chooses the following baseline models for comparison. To verify the performance of our model, we compare the performance differences between the baseline models in Since it takes a lot of time and energy for doctors to perform human qualitative evaluation, this paper chooses to randomly sample 500 data from the test set for this work. As shown in Si corresponding to the prescription result Hi with the highest overall evaluation of each baseline model. Putting Si into our GSCCAM to generate prescription Hi−GSCCAM. By plotting a thermodynamic chart of the symptoms-herbs relationship of Hi, Hi−GSCCAM, and Si-labeled prescription Pilabel, the performance of our model versus the baseline models is more intuitively shown. As shown in ith row and jth column represents the relationship between the ith herb and the jth symptom. Green represents correlation, yellow represents irrelevance, and the darker color represents the heavier degree. It is easy to see from the visualization that our model can produce prescription results that are closer to the ground truth when compared to all baseline models. This shows that our model can still take into account the relationship between symptoms and herbs while considering the external knowledge of TCM herbs. And our model can balance the external knowledge of TCM herbs and the compatibility principles of TCM, and satisfactorily complete the task of prescription generation.Considering that there may be inconsistencies in doctors' recognition of multiple prescriptions generated by the same model. To avoid the influence of the volatility of doctors' subjective judgments on the performance evaluation of the baseline models, we take out the symptoms In this section, to further evaluate the effectiveness of the proposed cross-concept knowledge source guidance module (CKGM), task-qualified knowledge base matching module (TKMM), multidimensional complementary attention module (MCAM), we conduct several ablation experiments. Under the same training settings and the same dataset, we separately verify the performance of the modules mentioned in GSCCAM and evaluate them according to the above automatic quantitative evaluation metrics.To verify the effectiveness of the CKGM branch network for the dual-branch task in the proposed method, this paper chooses the base model (BM) without the addition of CKGM to compare with our model. Meanwhile, to illustrate the applicability of the decoder structure in CKGM, this paper adjusts it to the decoder structure of GSCCAM for ablation experiments. As shown in To demonstrate the advantages of the TKMM, this paper compares the performance of our proposed GSCCAM with the model without TKMM. For simplicity, this paper only validates the effectiveness of TKMM on the basis of the model with the addition of the most effective dual-branch structure. The experimental results are shown in λ1 and λ2, this paper sets the different hyperparameter values by the traditional manual search method, so as to determine the best solution of the two similarity comparison methods. Since the hyperparameters λ1 and λ2 are synergistic, the basic condition of “λ1 + λ2 = 1.0” needs to be satisfied. Therefore, this paper sets the values of λ1 to , respectively, with 0.1 as the unit of change each time; λ2 corresponds to changing its value. As shown in λ1 and λ2 are 0.4 and 0.6, respectively. We believe that this is due to the better sensitivity of the Euclidean distance similarity calculation method and the advantage of the spatial vector similarity calculation method that does not need to be normalized. And the optimal results are obtained by combining the advantages of each. Therefore, this paper uses 0.4 and 0.6 as the optimal values of λ1 and λ2.For the setting of the hyperparameters k selected from the KB to achieve the best performance of our model, this paper uses the best values of λ1 and λ2. While this paper uses the traditional manual search method to set the value of k to [k is taken as 20, our model has the best performance. After analysis, we conclude that when k is taken as less than 20, the number of herbs in the HCP is too small, which limits the range of model selection. When k is taken as greater than 20, the number of herbs in the HCP is too large, which lose the significance of building a targeted set of candidates herbs. Therefore, our model selects 20 as the optimal value of k.In addition, in order to evaluate the value of the number of candidate prescriptions  of k to , 35 withIn order to demonstrate that the two candidate herb sets of the HCP and the CHL can promote each other during the model training process, this paper conducts ablation experiments on the multidimensional complementary attention module (MCAM). This paper removes the HCP and the CHL, respectively, and observes the performance changes of the no-candidate herbal set, the single-candidate herbal set and the double-candidate herbal set. The results are shown in In order to better demonstrate the feasibility and reliability of our model in practical applications, this paper selects two groups of symptoms and compares the similarity between prescriptions generated by our model, prescriptions generated by the baseline models, and label prescriptions, respectively. And at the same time, the human qualitative evaluation is performed by professional doctors. The details are shown in Photinia serratifolia is overlapped with the label content. Moreover, KDHR tends to predict high-frequency common herbs, such as Houpoea officinalis, Panax notoginseng, and Ziziphus jujuba. The number of generated herbs is far more than the actual required number. And the compatibility among herbs is very poor, almost irrelevant to the compatibility concept followed by TCM. PTM, as a topic model, likes to predict herbs with similar efficacy, such as Zingiber and Roasted zingiber and Coptis chinensis and Phellodendron. Meanwhile, although PTM considers the external knowledge of TCM, it is far from integrating the theoretical knowledge of TCM prescribing, and the predicted prescription also has few herbs that matched the label. In the prescription generated by Coverage-Soft-Model, only Photinia serratifolia is used to treat symptoms. Some of the herbs  not only do not play a curative role but even contradict the pathological nature of the symptoms. It may aggravate the condition of patient or even affect his life and health. Although Herb-Know can generate more reasonable prescriptions, the herbs Photinia serratifolia, Nepeta cataria, Roasted zingiber, and Corydalis decumbens, those are useful for the main symptoms account for less than 26.67% of the total herbs. And the rest of the herbs are mainly used to alleviate the subordinate symptoms. This shows that although Herb-Know can combine the knowledge of symptoms and herbs, its learning ability is limited. And it cannot better reflect the theoretical thinking of TCM prescribing. Three herbs  are prescribed by TPGen that overlapped with the label. Most of the remaining herbs are mild tonics that could not be used to treat symptoms, but would not harm the patient's health. Our GSCCAM yields prescription that is roughly close to the labeled prescription. At the same time, our model predicts that Photinia serratifolia, Nepeta cataria, Saposhnikovia divaricata and Zingiber all target the “deficient wind and wind evil”, and the main function of these herbs is to dispel wind. Although they are not exactly the same as the Photinia serratifolia, Dried zingiber and Ephedra used in the labeled prescription, they are the more reasonable combination than the labeled prescription. Angelica sinensis, dried Rehmannia glutinosa, and Angelica dahurica are not treating the main symptoms, but they can be used to nourish the blood, invigorate the blood and calm the wind, and relieve the condition. As for other herbs predicted by GSCCAM, such as Cinnamomi Ramulus, Scutellaria baicalensis georgi, and Glycyrrhiza can be used to lower ascending and floating Qi and clear heat; Asarum and Zanthoxylum ailanthoides can be used to tonify the Yang energy of the spleen and stomach; and Panax ginseng can nourish Qi and support correctness.For case 1, the similarity between the prescription predicted by KDHR and the label is very low, and only Mentha, Chrysanthemum, and other herbs those are used only for better eyesight. Prescriptions generated by PTM can be marginally effective in treating the symptoms. For example, Cinnamomum cassia can treat pain of the ribs under the armpit caused by the invasion of cold evil. However, because PTM has difficulty capturing higher-order correlations between herbs and symptoms, herbs with very similar efficacy (Dried zingiber and Zingiber) appear in the prescriptions it generated. Areca catechu, Zingiber, and Cinnamomum cassia generated by Coverage-Soft-Model all have a certain effect on the treatment of symptoms. However, Curcuma and Syzygium aromaticum are herbs that cannot be used together in the compatibility of TCM prescriptions, so this prescription has great side effects on the treatment of symptoms. Herb-Know generates herbs those can have a positive effect on treating symptoms. For example, the combination of Astragalus membranaceus and Eucommia ulmoides can nourish the liver and benefit Qi. However, there are still some herbs  in the prescription those can only achieve the most basic auxiliary functions for the treatment of symptoms. The similarity between the prescription generated by TPGen and the label prescription is relatively high. But the main feature of TPGen is the improvement of the model structure, which ignores the influence of TCM knowledge on prescription generation. So it generates herbs those are mild but useless for treating symptoms. The coincidence rate between the herbs predicted by our GSCCAM and the herbs in the label prescription is as high as 66.67%, and the remaining herbs not in the label are from herbal candidates. This group of symptoms in case 2 is mainly liver deficiency and cold invasion. In terms of the efficacy in treating symptoms, the herbs generated by our model have clear effects and reasonable collocation. For example, Aconitum carmichaeli can increase Yang Qi, dispel cold and relieve pain; Zingiber and Astragalus membranaceus can treat weakness and cold invasion; and Pericarpium Citri reticulata and Evodia rutaecarpa can nourish Yang Qi. From the overall performance of prescription, the prescription generated by our model is more reasonable than those generated by other baseline models.For case 2, KDHR generates far more herbs than the number of herbs in the label, but it generates prescription that is not appropriate for treating this group of symptoms. This shows that KDHR pays great attention to the influence of herbal properties on the generated prescriptions, but ignores the connections among herbs. Therefore, in the prescriptions generated by KDHR, there are Overall, our model does not just treat symptoms based on their correspondence with herbs. But it is influenced by the idea of potential compatibility in the prescription-based herbal candidates, analyzes and differentiates symptoms as professional TCM doctors do. And then it predicts appropriate herbs based on traditional prescription compatibility knowledge, ultimately forming a prescription with holistic, comprehensive, and correlative properties. It can also be seen through the human qualitative evaluation of doctors that our GSCCAM outperforms other baseline models. In some cases, the prescriptions predicted by our model are more reasonable than the labeled prescriptions.In this paper, we propose a novel Chinese herb guided and prescription candidates attention depth model based on symptoms text, which can guide the automatic generation of prescriptions based on theoretical knowledge of TCM. In the process of generating TCM prescriptions, our model can provide prescription-based herbal candidates targeted for treating symptoms according to the idea of prescription compatibility. To our knowledge, which is the first such attempt in artificial intelligence generation of TCM prescriptions, our model uses the DIEM to extract symptom-related information from prescriptions corresponding to symptoms. And, in order to better reflect the compatibility idea of TCM prescriptions, we add the TKMM in this model. We combine public and clinical datasets in the training and testing process of the model. In the experiments, we comprehensively analyze the performance of our GSCCAM through automatic quantitative evaluation metrics such as precision, recall, and F1 score and human qualitative evaluation metrics of professional TCM doctors. Ultimately, our model achieves 37.39% precision, 25.04% recall, and 29.99% F1 score, and achieves an average doctor score of 7.7 out of 10. The experimental results show that our model has achieved excellent automatic evaluation effect values, and has been highly recognized by TCM professional doctors. We believe that the research of this paper is full of prospective significance. This is not only because our study is based on the computation of a large amount of data but also because it is very consistent with the theoretical knowledge of traditional Chinese medicine. The research perspective of this paper breaks the shackles of the original fixed thinking in the study of combining TCM prescribing science with computer technology. It moves the research in this direction toward a more cutting-edge direction. We hope that this work can lay a foundation for future research on TCM prescription generation and attract more researchers to focus on the combination of TCM prescription and artificial intelligence.In future work, we plan to study the association among herbs, the evolution pattern of symptoms, and herbal dosage and other information related to prescription. At the same time, we hope to introduce external knowledge of TCM such as syndrome element identification and syndrome guidance into the model. In addition, we will continue to collect more clinical data to further improve the model performance. The combination of TCM and computer technology is a very promising research. Given the serious and responsible attitude towards scientific research and the idea of seeking realism and stability, we will continue to increase our investment in this research and steadily explore the future planned work very fully, and make more and better down-to-earth achievements for this research direction.


We report the case of a patient who underwent endovascular retrieval of a conical inferior vena cava (IVC) filter with a ruptured retraction hook that was attached to the IVC wall. A 21-year-old woman with a Celect (Cook) filter, implanted 1,522 days prior, requested retrieval. Preoperative ultrasound and CT examinations showed that the filter was inclined, the retraction hook was attached to the IVC wall, and one of the filter’s pedicles was broken. The inferior vena cava was patent, with no thrombus. Old superficial femoral vein thrombosis could be seen in the right lower extremity. The filter retrieval equipment  failed to capture the retraction hook. By means of a pigtail catheter (with a partly removed catheter tip) and loach guidewire, we applied a modified loop-snare technique to successfully cut the proliferative tissue near the tip of the retraction hook, by which the hook re-entered the inferior vena cava. Although the snare successfully captured the retraction hook and retrieved the filter, the broken pedicle was retained in the inferior vena cava. We used forceps to capture and pull it to the distal end. In the end, the inferior vena cava became patent, with no contrast agent spillage or residual, and no symptomatic pulmonary embolization. A simultaneous occurrence of oblique adherence and fracture is rarely found in the same filter; however, by using the modified loop-snare technique and biopsy forceps technique, we successfully retrieved the filter and broken pedicle. Our case provides a practical auxiliary technique for regular clinical practice. The American College of Chest Physicians’ (ACCP) 2016 guidelines for the management of venous thromboembolism state that an inferior vena cava (IVC) filter should be removed immediately once the risk of pulmonary embolism has decreased . EndovasHere, we report the case of a 21-year-old woman who requested retrieval of a Celect IVC filter, which had been implanted 1,522 days ago. The patient had been in a traffic accident more than 4 years ago, which had caused pelvic fracture, multiple fractures of the lower extremity, rib fracture with pleural effusion, and hemorrhage in the subarachnoid space. She had been treated with multiple surgeries. Due to deep vein thrombosis in the right lower extremity, the Celect filter was implanted  to prevent fatal pulmonary embolization. She also underwent open internal fixation surgery for unstable pelvic fractures and closed intramedullary nailing for femoral shaft fractures. The patient had been discharged without postoperative anticoagulation therapy due to factors such as a low hemoglobin count (7.2 g/L), subarachnoid hemorrhage, and pleural effusion. She returned to the local high school to continue her education, and no follow-up had been done in the last 4 years.The patient complained of dull pain in the right lower abdomen 3 months before admission. Upon admission, venous ultrasound showed an old superficial femoral vein thrombosis in the right lower extremity, but no thrombosis was found in the inferior vena cava. An enhanced abdominal CT also showed that the inferior vena cava was patent, with no hematoma visible. However, the filter was severely inclined, the retraction hook was adhered to the vena cava wall, the main pedicle of the filter was broken, and the broken pedicle was embedded in the wall of the inferior vena cava. Due to the adherence of the retraction hook to the wall and the presence of the broken pedicle, the preferred treatment options were to retrieve the filter by the endovascular method with minimal invasiveness or retrieve the filter laparoscopically . Open suAngiography via the right common femoral venous access showed that no thrombosis was found in the inferior vena cava, and that it was patent; it also showed that the main pedicle of the Celect filter was broken, the filter was obliquely attached to the retraction hook, and proliferative tissue surrounded the hook . A filteThe filter retrieval set and biopsy forceps were introduced via the right common femoral venous access, and the broken pedicle was successfully captured . HoweverThe broken pedicle was captured again, and the biopsy forceps and the retrieval sheath were used to pull the broken pedicle to the distal end. Then, the angle and coaxial distance were gradually reduced. The broken pedicle was entered into the retrieval sheath  and was No symptomatic pulmonary embolization occurred during the perioperative period, and the patient received oral rivaroxaban 20 mg QD as an anticoagulation therapy  postoperThe possible complications of long-term IVC filter implantation include obstruction and perforation of the vena cava and approaching or entering of the filter pedicle into the surrounding organs, which can manifest with associated symptoms –15. WhenDue to abnormalities in the inferior vena cava, the presence of thrombus blocked by the filter, the consequences of the IVC filter placement, or other unknown reasons, the conical filter is more prone to adhere obliquely. Furthermore, the attachment of the retraction hook to proliferative tissues can easily result in failure to capture the hook. The following methods could increase the chances of successfully retrieving the filter: (1) Use of the loop-snare technique  to form The fracture rate of the Celect filter is only 1.1%, which is the lowest rate among all kinds of IVC filters . NeverthThe patient in our study experienced two complications: oblique adherence and fracture of the filter. The modified loop-snare technique and biopsy forceps technique were used to resolve these complications, with an initial failure to capture the retraction hook and retrieve the broken pedicle and filter. There are few similar reports in the recent literature. In contrast to the use of a reverse-curve catheter, as reported by Desai et al. , the looOur surgical method has the following limitations: (1) The head of the pigtail catheter is not robust, and it is therefore easily bent or deformed. The guidewire can be introduced into the pigtail catheter end to increase the supporting force ; (2) The presence of a gap between the filter and the inferior vena cava wall is critical to the success of the modified loop-snare technique. When the filter is severely tilted or deformed, there may be no gap to allow the guidewire and catheter to pass through, making the procedure less successful. (3) A preoperative enhanced CT of the abdomen is a good way to evaluate and initially select the loop-snare technique.It should be noted that the technique of grasping a broken pedicle using biopsy forceps cannot be performed routinely, and there is a risk of the pedicle falling off. A balloon can be used to block the inferior vena cava at the proximal end, which may reduce the risk of the broken pedicle falling off into the pulmonary artery.Longer retention times can spell more serious damage for the inferior vena cava when the filter is retrieved, especially if any complications such as a broken pedicle are present. Regular postoperative anticoagulation therapy and follow-up are therefore indispensable. In accordance with the ACCP guidelines , 2, 11, In summary, oblique adherence and fracture of conical IVC filters are common complications, but the simultaneous occurrence of both in the same filter is rare. The filter and broken pedicle were removed successfully using a modified loop-snare technique and biopsy forceps technique, avoiding the major trauma of open surgery. Our method can therefore serve as an auxiliary technique for regular clinical practice.