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

ABSTRACT:

Objective

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).

Materials and methods

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 phenot

SUBMITTER: Liao KP 

PROVIDER: S-EPMC6798574 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature