Mar 23, 2019

High-throughput Multimodal Automated Phenotyping (MAP) with Application to PheWAS

BioRxiv : the Preprint Server for Biology
Katherine P. Liaowith the VA Million Veteran Program

Abstract

Objective Electronic health records (EHR) 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). Method We developed a mapping method for automatically identifying relevant ICD and NLP concepts for a specific phenotype leveraging the UMLS. Aggregated ICD and NLP counts along with healthcare utilization were jointly analyzed by fitting an ensemble of latent mixture models. The MAP algorithm yields a predicted probability of phenotype for each patient and a threshold for classifying subjects with phenotype yes/no. The algorithm was validated using labeled data for 16 phenotypes from a biorepository and further tested in an independent cohort PheWAS for two SNPs with known associations. Results The MAP algorithm achieved higher or similar AUC and F-scores compared to the ICD code across all 16 phenotypes. The features assembled via the automated approach had comparable accuracy to those assembl...Continue Reading

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Mentioned in this Paper

Study
Electronic Health Records
Classification
Definition
Unified Medical Language System
Translational Research
Bnk protein, Drosophila
Implantable Defibrillator
NLP 1
Single Nucleotide Polymorphism

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