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Clinical trial cohort selection based on multi-level rule-based natural language processing system.

Long Chen1, Yu Gu1, Xin Ji1

  • 1Med Data Quest, Inc, La Jolla, California, USA.

Journal of the American Medical Informatics Association : JAMIA
|July 14, 2019
PubMed
Summary
This summary is machine-generated.

A clinical natural language processing (NLP) system effectively identifies eligible patients for clinical trials using medical records. A rule-based NLP approach achieved high performance, demonstrating its potential for cohort selection.

Keywords:
UMLSclinical natural language processingclinical trialcohort selectionrule-based system

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Area of Science:

  • Biomedical Informatics
  • Clinical Research Informatics

Background:

  • Patient identification for clinical trials is complex and labor-intensive.
  • Automating this process can significantly improve trial efficiency.

Purpose of the Study:

  • To develop and evaluate a clinical natural language processing (NLP) system for automated patient eligibility assessment.
  • To compare the performance of a rule-based NLP system with a general clinical NLP (cNLP) system.

Main Methods:

  • Developed an integrated rule-based clinical NLP system with task-specific knowledge.
  • Implemented and evaluated a general cNLP system using the Unified Medical Language System (UMLS) and Unstructured Information Management Architecture (UIMA).
  • Evaluated systems in the 2018 National NLP Clinical Challenges (n2c2) Shared-Task.

Main Results:

  • The rule-based system achieved an F-measure of 0.9028, ranking fourth in the n2c2 challenge.
  • The rule-based system's performance was competitive with the top-ranked system.
  • The general cNLP system showed potential in clinical concept extraction.

Conclusions:

  • A well-designed rule-based clinical NLP system can achieve high performance in cohort selection, even with limited training data.
  • A hybrid approach combining rule-based and general cNLP systems shows promise for exceeding current state-of-the-art performance.