Open AccessEfficient and Accurate Extracting of Unstructured EHRs on Cancer Therapy Responses for the Development of RECIST Natural Language Processing Tools: Part I, the Corpus
Author(s) -
Yalun Li,
YungHung Luo,
Jason A. Wampfler,
Samuel M. Rubinstein,
Firat Tiryaki,
V. Kumar Ashok,
Jeremy L. Warner,
Hua Xu,
Ping Yang
Publication year - 2020
Publication title -
jco clinical cancer informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.188
H-Index - 12
ISSN - 2473-4276
DOI - 10.1200/cci.19.00147
Subject(s) - computer science , annotation , natural language processing , response evaluation criteria in solid tumors , set (abstract data type) , medicine , information retrieval , medical record , artificial intelligence , medical physics , clinical trial , surgery , phases of clinical research , programming language
Electronic health records (EHRs) are created primarily for nonresearch purposes; thus, the amounts of data are enormous, and the data are crude, heterogeneous, incomplete, and largely unstructured, presenting challenges to effective analyses for timely, reliable results. Particularly, research dealing with clinical notes relevant to patient care and outcome is seldom conducted, due to the complexity of data extraction and accurate annotation in the past. RECIST is a set of widely accepted research criteria to evaluate tumor response in patients undergoing antineoplastic therapy. The aim for this study was to identify textual sources for RECIST information in EHRs and to develop a corpus of pharmacotherapy and response entities for development of natural language processing tools.