Developing Customizable Cancer Information Extraction Modules for Pathology Reports Using CLAMP
Year: 2019
Type: book-chapter
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.
DOI: 10.3233/shti190383
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
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: hybrid
Awards: Advancing Cancer Pharmacoepidemiology Research Through EHRs and Informatics, Natural language processing for clinical and translational research, Informatics Tools for Pharmacogenomic Discovery using Practice-based Data, Interactive machine learning methods for clinical natural language processing, Patient Medical History Representation, Extraction, and Inference from EHR Data
OpenAlex ID: W2990377124