A review of approaches to identifying patient phenotype cohorts using electronic health records. Review uri icon

Overview

MeSH

  • Diagnosis
  • Humans
  • Phenotype
  • Statistics as Topic
  • Vocabulary, Controlled

MeSH Major

  • Artificial Intelligence
  • Data Mining
  • Electronic Health Records
  • Natural Language Processing

abstract

  • To summarize literature describing approaches aimed at automatically identifying patients with a common phenotype. We performed a review of studies describing systems or reporting techniques developed for identifying cohorts of patients with specific phenotypes. Every full text article published in (1) Journal of American Medical Informatics Association, (2) Journal of Biomedical Informatics, (3) Proceedings of the Annual American Medical Informatics Association Symposium, and (4) Proceedings of Clinical Research Informatics Conference within the past 3 years was assessed for inclusion in the review. Only articles using automated techniques were included. Ninety-seven articles met our inclusion criteria. Forty-six used natural language processing (NLP)-based techniques, 24 described rule-based systems, 41 used statistical analyses, data mining, or machine learning techniques, while 22 described hybrid systems. Nine articles described the architecture of large-scale systems developed for determining cohort eligibility of patients. We observe that there is a rise in the number of studies associated with cohort identification using electronic medical records. Statistical analyses or machine learning, followed by NLP techniques, are gaining popularity over the years in comparison with rule-based systems. There are a variety of approaches for classifying patients into a particular phenotype. Different techniques and data sources are used, and good performance is reported on datasets at respective institutions. However, no system makes comprehensive use of electronic medical records addressing all of their known weaknesses.

publication date

  • March 2014
  • April 2014

has subject area

  • Artificial Intelligence
  • Data Mining
  • Diagnosis
  • Electronic Health Records
  • Humans
  • Natural Language Processing
  • Phenotype
  • Statistics as Topic
  • Vocabulary, Controlled

Research

keywords

  • Journal Article
  • Review

Identity

Language

  • eng

PubMed Central ID

  • PMC3932460

Digital Object Identifier (DOI)

  • 10.1136/amiajnl-2013-001935

PubMed ID

  • 24201027

Additional Document Info

start page

  • 221

end page

  • 230

volume

  • 21

number

  • 2