Developing Customizable Cancer Information Extraction Modules for Pathology Reports Using CLAMP.

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ID: 45411
2019
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Ranked #5 of 170 articles by views in Studies in health technology and informatics

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Abstract
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.
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soysal2019developingstudies Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Soysal, Ergin;Warner, Jeremy L;Wang, Jingqi;Jiang, Min;Harvey, Krysten;Jain, Sandeep Kumar;Dong, Xiao;Song, Hsing-Yi;Siddhanamatha, Harish;Wang, Liwei;Dai, Qi;Chen, Qingxia;Du, Xianglin;Tao, Cui;Yang, Ping;Denny, Joshua Charles;Liu, Hongfang;Xu, Hua;
Journal Studies in health technology and informatics
Year 2019
DOI
10.3233/SHTI190383
URL
Keywords
electronic health records natural language processing information storage and retrieval

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