Risk factor detection for heart disease by applying text analytics in electronic medical records

Manabu Torii, Jung Wei Fan, Wei li Yang, Theodore Lee, Matthew T. Wiley, Daniel S. Zisook, Yang Huang

Research output: Contribution to journalArticle

15 Scopus citations

Abstract

In the United States, about 600,000 people die of heart disease every year. The annual cost of care services, medications, and lost productivity reportedly exceeds 108.9 billion dollars. Effective disease risk assessment is critical to prevention, care, and treatment planning. Recent advancements in text analytics have opened up new possibilities of using the rich information in electronic medical records (EMRs) to identify relevant risk factors. The 2014 i2b2/UTHealth Challenge brought together researchers and practitioners of clinical natural language processing (NLP) to tackle the identification of heart disease risk factors reported in EMRs. We participated in this track and developed an NLP system by leveraging existing tools and resources, both public and proprietary. Our system was a hybrid of several machine-learning and rule-based components. The system achieved an overall F1 score of 0.9185, with a recall of 0.9409 and a precision of 0.8972.

Original languageEnglish (US)
Pages (from-to)S164-S170
JournalJournal of Biomedical Informatics
Volume58
DOIs
StatePublished - Dec 1 2015
Externally publishedYes

    Fingerprint

Keywords

  • Medical records
  • Natural language processing
  • Risk assessment
  • Text classification

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics

Cite this