A survey of clinical phenotyping in selected national networks: demonstrating the need for high-throughput, portable, and computational methods Academic Article uri icon

Overview

MeSH Major

  • Electronic Health Records
  • Heart Failure
  • Medical Informatics

abstract

  • © 2016 Elsevier B.V.Objective The combination of phenomic data from electronic health records (EHR) and clinical data repositories with dense biological data has enabled genomic and pharmacogenomic discovery, a first step toward precision medicine. Computational methods for the identification of clinical phenotypes from EHR data will advance our understanding of disease risk and drug response, and support the practice of precision medicine on a national scale. Methods Based on our experience within three national research networks, we summarize the broad approaches to clinical phenotyping and highlight the important role of these networks in the progression of high-throughput phenotyping and precision medicine. We provide supporting literature in the form of a non-systematic review. Results The practice of clinical phenotyping is evolving to meet the growing demand for scalable, portable, and data driven methods and tools. The resources required for traditional phenotyping algorithms from expert defined rules are significant. In contrast, machine learning approaches that rely on data patterns will require fewer clinical domain experts and resources. Conclusions Machine learning approaches that generate phenotype definitions from patient features and clinical profiles will result in truly computational phenotypes, derived from data rather than experts. Research networks and phenotype developers should cooperate to develop methods, collaboration platforms, and data standards that will enable computational phenotyping and truly modernize biomedical research and precision medicine.

publication date

  • July 2016

Research

keywords

  • Academic Article

Identity

Language

  • eng

PubMed Central ID

  • PMC5480212

Digital Object Identifier (DOI)

  • 10.1016/j.artmed.2016.05.005

PubMed ID

  • 27506131

Additional Document Info

start page

  • 57

end page

  • 61

volume

  • 71