Efficient and accurate extracting of unstructured EHRs on cancer therapy responses for the development of RECIST natural language processing tools: Part I, the corpus

Yalun Li, Yung Hung Luo, Jason A. Wampfler, Samuel M. Rubinstein, Firat Tiryaki, V. Kumar Ashok, Jeremy L. Warner, Hua Xu, Ping Yang

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

PURPOSE 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. METHODS We focused on pharmacotherapies and patient responses, using 55,120 medical notes (n = 72 types) in Mayo Clinic's EHRs from 622 randomly selected patients who signed authorization for research. Using the Multidocument Annotation Environment tool, we applied and evaluated predefined keywords, and time interval and note-type filters for identifying RECIST information and established a gold standard data set for patient outcome research. RESULTS Key words reduced clinical notes to 37,406, and using four note types within 12 months postdiagnosis further reduced the number of notes to 5,005 that were manually annotated, which covered 97.9% of all cases (n = 609 of 622). The resulting data set of 609 cases (n = 503 for training and n = 106 for validation purpose), contains 736 fully annotated, deidentified clinical notes, with pharmacotherapies and four response end points: complete response, partial response, stable disease, and progressive disease. This resource is readily expandable to specific drugs, regimens, and most solid tumors. CONCLUSION We have established a gold standard data set to accommodate development of biomedical informatics tools in accelerating research into antineoplastic therapeutic response.

Original languageEnglish (US)
Pages (from-to)383-391
Number of pages9
JournalJCO Clinical Cancer Informatics
Volume4
DOIs
StatePublished - 2020

ASJC Scopus subject areas

  • Oncology
  • Health Informatics
  • Cancer Research

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