Developing Customizable Cancer Information Extraction Modules for Pathology Reports Using CLAMP.
Natural language processing (NLP) technologies have been successfully applied to cancer research by enabling automated phenotypic information extraction from narratives in electronic health records (EHRs) such as pathology reports; however, developing customized NLP solutions requires substantial effort. To facilitate the adoption of NLP in cancer research, we have developed a set of customizable modules for extracting comprehensive types of cancer-related information in pathology reports (e.g., tumor size, tumor stage, and biomarkers), by leveraging the existing CLAMP system, which provides user-friendly interfaces for building customized NLP solutions for individual needs. Evaluation using annotated data at Vanderbilt University Medical Center showed that CLAMP-Cancer could extract diverse types of cancer information with good F-measures (0.80-0.98). We then applied CLAMP-Cancer to an information extraction task at Mayo Clinic and showed that we can quickly build a customized NLP system with comparable performance with an existing system at Mayo Clinic. CLAMP-Cancer is freely available for academic use.
['Soysal|Ergin|E|', 'Warner|Jeremy L|JL|', 'Wang|Jingqi|J|', 'Jiang|Min|M|', 'Harvey|Krysten|K|', 'Jain|Sandeep Kumar|SK|', 'Dong|Xiao|X|', 'Song|Hsing-Yi|HY|', 'Siddhanamatha|Harish|H|', 'Wang|Liwei|L|', 'Dai|Qi|Q|', 'Chen|Qingxia|Q|', 'Du|Xianglin|X|', 'Tao|Cui|C|', 'Yang|Ping|P|', 'Denny|Joshua Charles|JC|', 'Liu|Hongfang|H|', 'Xu|Hua|H|']
D057286:Electronic Health Records D006801:Humans D016247:Information Storage and Retrieval D009323:Natural Language Processing D009369:Neoplasms D058028:Research Report
Natural Language Processing Electronic Health Records Information Storage and Retrieval