Text Mining of the Electronic Health Record: An Information Extraction Approach for Automated Identification and Subphenotyping of HFpEF Patients for Clinical Trials

Siddhartha R. Jonnalagadda*, Abhishek K. Adupa, Ravi P. Garg, Jessica Corona-Cox, Sanjiv J. Shah

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

17 Scopus citations

Abstract

Precision medicine requires clinical trials that are able to efficiently enroll subtypes of patients in whom targeted therapies can be tested. To reduce the large amount of time spent screening, identifying, and recruiting patients with specific subtypes of heterogeneous clinical syndromes (such as heart failure with preserved ejection fraction [HFpEF]), we need prescreening systems that are able to automate data extraction and decision-making tasks. However, a major obstacle is the vast amount of unstructured free-form text in medical records. Here we describe an information extraction-based approach that automatically converts unstructured text into structured data, which is cross-referenced against eligibility criteria using a rule-based system to determine which patients qualify for a major HFpEF clinical trial (PARAGON). We show that we can achieve a sensitivity and positive predictive value of 0.95 and 0.86, respectively. Our open-source algorithm could be used to efficiently identify and subphenotype patients with HFpEF and other disorders.

Original languageEnglish (US)
Pages (from-to)313-321
Number of pages9
JournalJournal of Cardiovascular Translational Research
Volume10
Issue number3
DOIs
StatePublished - Jun 1 2017

Keywords

  • Clinical trials
  • Information extraction
  • Natural language processing
  • Precision medicine

ASJC Scopus subject areas

  • Molecular Medicine
  • Genetics
  • Pharmaceutical Science
  • Cardiology and Cardiovascular Medicine
  • Genetics(clinical)

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