Oct 16, 2019

High-throughput multimodal automated phenotyping (MAP) with application to PheWAS

Journal of the American Medical Informatics Association : JAMIA
Katherine P LiaoTianxi Cai

Abstract

Electronic health records linked with biorepositories are a powerful platform for translational studies. A major bottleneck exists in the ability to phenotype patients accurately and efficiently. The objective of this study was to develop an automated high-throughput phenotyping method integrating International Classification of Diseases (ICD) codes and narrative data extracted using natural language processing (NLP). We developed a mapping method for automatically identifying relevant ICD and NLP concepts for a specific phenotype leveraging the Unified Medical Language System. Along with health care utilization, aggregated ICD and NLP counts were jointly analyzed by fitting an ensemble of latent mixture models. The multimodal automated phenotyping (MAP) algorithm yields a predicted probability of phenotype for each patient and a threshold for classifying participants with phenotype yes/no. The algorithm was validated using labeled data for 16 phenotypes from a biorepository and further tested in an independent cohort phenome-wide association studies (PheWAS) for 2 single nucleotide polymorphisms with known associations. The MAP algorithm achieved higher or similar AUC and F-scores compared to the ICD code across all 16 phenoty...Continue Reading

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Citations

Mentioned in this Paper

Genome-Wide Association Study
Study
Electronic Health Records
Classification
Natural Products
Definition
Unified Medical Language System
Participant
Translational Research
Combined Modality Therapy

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