Neural-network analysis of socio-medical data to identify predictors of undiagnosed hepatitis C virus infections in Germany (DETECT).
BACKGROUND
Chronic hepatitis C virus (HCV)-infection is a slowly debilitating and potentially fatal disease with a high estimated number of undiagnosed cases. Given the major advances in the treatment, detection of unreported infections is a consequential step for eliminating hepatitis C on a population basis. The prevalence of chronic hepatitis C is, however, low in most countries making mass screening neither cost effective nor practicable.


METHODS
We used a Kohonen artificial neural network (ANN) to analyze socio-medical data of 1.8 million insurants for predictors of undiagnosed HCV infections. The data had to be anonymized due to ethical requirements. The network was trained with variables obtained from a subgroup of 2544 patients with confirmed hepatitis C-virus (HCV) infections excluding variables directly linked to the diagnosis of HCV. All analyses were performed using the data mining solution "RayQ". Training results were visualized three-dimensionally and the distributions and characteristics of the clusters were explored within the map.


RESULTS
All 2544 patients with confirmed chronic HCV diagnoses were localized in a clearly defined cluster within the Kohonen self-organizing map. An additional 2217 patients who had not been diagnosed with hepatitis C co-localized to the same cluster, indicating socio-medical similarities and a potentially elevated risk of infection. Several factors including, age, diagnosis codes and drug prescriptions acted only in conjunction as predictors of an elevated HCV risk.


CONCLUSIONS
This ANN approach may allow for a more efficient risk adapted HCV-screening. However, further validation of the prediction model is required.
['Reiser|Markus|M|0000-0002-0096-7903', 'Wiebner|Bianka|B|', 'Hirsch|Jürgen|J|', '|||']
D000067575:Administrative Claims, Healthcare D000328:Adult D000368:Aged D001185:Artificial Intelligence D058345:Asymptomatic Infections D015331:Cohort Studies D057225:Data Mining D016208:Databases, Factual D057286:Electronic Health Records D005260:Female D005858:Germany D019698:Hepatitis C, Chronic D006801:Humans D008297:Male D008403:Mass Screening D008875:Middle Aged D016571:Neural Networks, Computer D015995:Prevalence D012307:Risk Factors D012959:Socioeconomic Factors D000080842:Undiagnosed Diseases
Chronic hepatitis C Screening Artificial neural network Data mining Artificial intelligence