Journal
JOURNAL OF BIOMEDICAL INFORMATICS
Volume 58, Issue -, Pages 156-165Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jbi.2015.10.001
Keywords
Probabilistic modeling; Computational disease models; Phenotyping; Clinical phenotype modeling; Medical information systems; Electronic health record
Funding
- National Science Foundation (NSF) [1344668]
- NSF IGERT [1144854]
- NLM [T15LM007079]
- Division Of Graduate Education
- Direct For Education and Human Resources [1144854] Funding Source: National Science Foundation
- Div Of Information & Intelligent Systems
- Direct For Computer & Info Scie & Enginr [1344668] Funding Source: National Science Foundation
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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. (C) 2015 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license.
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