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

Katherine P Liao, Tianxi Cai, Guergana K Savova, Shawn N Murphy, Elizabeth W Karlson, Ashwin N Ananthakrishnan, Vivian S Gainer, Stanley Y Shaw, Zongqi Xia, Peter Szolovits, Susanne Churchill, Isaac Kohane, Katherine P Liao, Tianxi Cai, Guergana K Savova, Shawn N Murphy, Elizabeth W Karlson, Ashwin N Ananthakrishnan, Vivian S Gainer, Stanley Y Shaw, Zongqi Xia, Peter Szolovits, Susanne Churchill, Isaac Kohane

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.

Conflict of interest statement

Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/coi_disclosure.pdf (available on request from the corresponding author) and declare: support from US National Institutes of Health and the Harold and Duval Bowen Fund for the submitted work; KPL is supported by the National Institutes of Health and the Harold and Duval Bowen Fund; GKS is on the Advisory Board of Wired Informatics, which provides services and products for clinical NLP applications; no other relationships or activities that could appear to have influenced the submitted work.

Figures

https://www.ncbi.nlm.nih.gov/pmc/articles/instance/4784791/bin/liak023069.f1_default.jpg
Fig 1 Overview of the two main types of EMR data, structured and unstructured, and how these data can be integrated for research studies. In this instance, the figure illustrates the development of a phenotype algorithm for rheumatoid arthritis. *Including ICD-9 (international classification of diseases, 9th revision) codes and CPT (current procedural terminology) codes
https://www.ncbi.nlm.nih.gov/pmc/articles/instance/4784791/bin/liak023069.f2_default.jpg
Fig 2 Overview of methods used to develop EMR phenotype algorithms
https://www.ncbi.nlm.nih.gov/pmc/articles/instance/4784791/bin/liak023069.f3_default.jpg
Fig 3 Proportion of patients in data marts for rheumatoid arthritis (n=28 982) and ulcerative colitis (n=14 335) who have been classified to have the phenotype. Numbers over each bar=EMR cohort size

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Source: PubMed

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