High-throughput multimodal automated phenotyping (MAP) with application to PheWAS
Name
587436v1.full.pdf
Description
Submitted version
Size
509.32 KB
Format
Unknown
Checksum (MD5)
232948e2d626b61dfa6331a57971ed73
Author(s)
Liao, Katherine P
Sun, Jiehuan
Cai, Tianrun A
Link, Nicholas
Hong, Chuan
Huang, Jie
Huffman, Jennifer E
Gronsbell, Jessica
Zhang, Yichi
Ho, Yuk-Lam
Date Issued
2019
Journal
Journal of the American Medical Informatics Association
Publisher
Oxford University Press (OUP)
Version
Original manuscript
Abstract
© 2019 The Author(s). 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 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. 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 assembled via manual curation (AUCMAP 0.943, AUCmanual 0.941). The PheWAS results suggest that the MAP approach detected previously validated associations with higher power when compared to the standard PheWAS method based on ICD codes. Conclusion: The MAP approach increased the accuracy of phenotype definition while maintaining scalability, thereby facilitating use in studies requiring large-scale phenotyping, such as PheWAS.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1093/JAMIA/OCZ066
