Work

Development of phenotype algorithms using electronic medical records and incorporating natural language processing

Year: 2015

Type: article

Abstract: Electronic medical records are emerging as a major source of data for clinical and translational research studies, although phenotypes of interest need to be accurately defined first. This article provides an overview of how to develop a phenotype algorithm from electronic medical records, incorporating modern informatics and biostatistics methods.

Source: BMJ

DOI: 10.1136/bmj.h1885

Authors: K. P. Liao, T. Cai, G. K. Savova, S. N. Murphy, E. W. Karlson, A. N. Ananthakrishnan, V. S. Gainer, S. Y. Shaw, Z. Xia, P. Szolovits, S. Churchill, I. Kohane

Institutions: Brigham and Women's Hospital, Harvard University, Boston Children's Hospital, Massachusetts General Hospital, Center for Systems Biology, Massachusetts Institute of Technology

Language: English

Topic: Machine Learning in Healthcare

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: bronze

Funders: Massachusetts General Hospital, National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases, National Institute of Neurological Disorders and Stroke, National Institute of Arthritis and Musculoskeletal and Skin Diseases, U.S. National Library of Medicine

Awards: G1206153511, G3207435465, G5105562027, Leveraging genetics and environment to predict presymptomatic multiple sclerosis, G5529003676, G5605408157, G9000027757, G9000045734, G9000054525, Center for the Study of Inflammatory Bowel Disease at Massachusetts General Hospital

OpenAlex ID: W2138162199

FWCI: 25.19

Cites: 37

Cited by: 280

Related to: 10