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dc.contributor.authorLiao, Katherine P
dc.contributor.authorSun, Jiehuan
dc.contributor.authorCai, Tianrun A
dc.contributor.authorLink, Nicholas
dc.contributor.authorHong, Chuan
dc.contributor.authorHuang, Jie
dc.contributor.authorHuffman, Jennifer E
dc.contributor.authorGronsbell, Jessica
dc.contributor.authorZhang, Yichi
dc.contributor.authorHo, Yuk-Lam
dc.contributor.authorCastro, Victor
dc.contributor.authorGainer, Vivian
dc.contributor.authorMurphy, Shawn N
dc.contributor.authorO’Donnell, Christopher J
dc.contributor.authorGaziano, J Michael
dc.contributor.authorCho, Kelly
dc.contributor.authorSzolovits, Peter
dc.contributor.authorKohane, Isaac S
dc.contributor.authorYu, Sheng
dc.contributor.authorCai, Tianxi
dc.date.accessioned2021-10-27T19:57:50Z
dc.date.available2021-10-27T19:57:50Z
dc.date.issued2019
dc.identifier.urihttps://hdl.handle.net/1721.1/134057
dc.description.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.
dc.language.isoen
dc.publisherOxford University Press (OUP)
dc.relation.isversionof10.1093/JAMIA/OCZ066
dc.rightsCreative Commons Attribution-Noncommercial-Share Alike
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/
dc.sourcebioRxiv
dc.titleHigh-throughput multimodal automated phenotyping (MAP) with application to PheWAS
dc.typeArticle
dc.relation.journalJournal of the American Medical Informatics Association
dc.eprint.versionOriginal manuscript
dc.type.urihttp://purl.org/eprint/type/JournalArticle
eprint.statushttp://purl.org/eprint/status/NonPeerReviewed
dc.date.updated2021-01-26T19:09:17Z
dspace.orderedauthorsLiao, KP; Sun, J; Cai, TA; Link, N; Hong, C; Huang, J; Huffman, JE; Gronsbell, J; Zhang, Y; Ho, Y-L; Castro, V; Gainer, V; Murphy, SN; O’Donnell, CJ; Gaziano, JM; Cho, K; Szolovits, P; Kohane, IS; Yu, S; Cai, T
dspace.date.submission2021-01-26T19:09:27Z
mit.journal.volume26
mit.journal.issue11
mit.licenseOPEN_ACCESS_POLICY
mit.metadata.statusAuthority Work and Publication Information Needed


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