title: Learning probabilistic phenotypes from heterogeneous EHR data.
abstract: We present the Unsupervised Phenome Model (UPhenome), a probabilistic graphical model for large-scale discovery of computational models of disease, or phenotypes. We tackle this challenge through the joint modeling of a large set of diseases and a large set of clinical observations. The observations are drawn directly from heterogeneous patient record data (notes, laboratory tests, medications, and diagnosis codes), and the diseases are modeled in an unsupervised fashion. We apply UPhenome to two qualitatively different mixtures of patients and diseases: records of extremely sick patients in the intensive care unit with constant monitoring, and records of outpatients regularly followed by care providers over multiple years. We demonstrate that the UPhenome model can learn from these different care settings, without any additional adaptation. Our experiments show that (i) the learned phenotypes combine the heterogeneous data types more coherently than baseline LDA-based phenotypes; (ii) they each represent single diseases rather than a mix of diseases more often than the baseline ones; and (iii) when applied to unseen patient records, they are correlated with the patients' ground-truth disorders. Code for training, inference, and quantitative evaluation is made available to the research community.
authors.affiliation: Department of Biomedical Informatics, Columbia University, 622 W. 168th Street, New York, NY, USA. Electronic address: rip7002@nyp.org.
authors.forename: Rimma
authors.initials: R
authors.lastname: Pivovarov
authors.affiliation: Department of Biomedical Informatics, Columbia University, 622 W. 168th Street, New York, NY, USA. Electronic address: ajp2120@cumc.columbia.edu.
authors.forename: Adler J
authors.initials: AJ
authors.lastname: Perotte
authors.affiliation: Department of Biomedical Informatics, Columbia University, 622 W. 168th Street, New York, NY, USA. Electronic address: eg2795@cumc.columbia.edu.
authors.forename: Edouard
authors.initials: E
authors.lastname: Grave
authors.affiliation: College of Physicians and Surgeons, Columbia University, New York, NY, USA. Electronic address: ja2686@cumc.columbia.edu.
authors.forename: John
authors.initials: J
authors.lastname: Angiolillo
authors.affiliation: Department of Applied Physics and Applied Mathematics, Columbia University, New York, NY, USA. Electronic address: chw2@columbia.edu.
authors.forename: Chris H
authors.initials: CH
authors.lastname: Wiggins
authors.affiliation: Department of Biomedical Informatics, Columbia University, 622 W. 168th Street, New York, NY, USA. Electronic address: noemie.elhadad@columbia.edu.
authors.forename: Noémie
authors.initials: N
authors.lastname: Elhadad
mesh_terms: D057286:Electronic Health Records; D006801:Humans; D007858:Learning; D010641:Phenotype; D011336:Probability
keywords: Clinical phenotype modeling; Computational disease models; Electronic health record; Medical information systems; Phenotyping; Probabilistic modeling