Format

Send to

Choose Destination
Artif Intell Med. 2016 Jul;71:57-61. doi: 10.1016/j.artmed.2016.05.005. Epub 2016 Jun 25.

Clinical phenotyping in selected national networks: demonstrating the need for high-throughput, portable, and computational methods.

Author information

1
Duke University School of Nursing, 311 Trent Drive, Durham, NC 27710 USA. Electronic address: rachel.richesson@duke.edu.
2
School of Computational Science and Engineering, Georgia Institute of Technology, 266 Ferst Drive, Atlanta, GA 30313, USA. Electronic address: jsun@cc.gatech.edu.
3
Department of Health Sciences Research, 200 1st Street SW, Mayo Clinic, Rochester, MN, 55905, USA. Electronic address: pathak@med.cornell.edu.
4
Departments of Medicine and Preventive Medicine, Northwestern University, 633 N St. Clair St. 20th floor. Chicago IL 60611, USA. Electronic address: a-kho@northwestern.edu.
5
Departments of Biomedical Informatics and Medicine, Vanderbilt University, 2525 West End Ave, Suite 672, Nashville, TN 37203, USA. Electronic address: josh.denny@Vanderbilt.Edu.

Abstract

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.

KEYWORDS:

Clinical phenotyping; Electronic health records; Machine learning; Networked research; Precision medicine

PMID:
27506131
PMCID:
PMC5480212
[Available on 2017-07-01]
DOI:
10.1016/j.artmed.2016.05.005
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Elsevier Science Icon for PubMed Central
Loading ...
Support Center