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dc.contributor.authorHorvitz, Eric
dc.contributor.authorWiens, Jenna Anne Marleau
dc.contributor.authorGuttag, John V
dc.date.accessioned2018-07-02T17:08:19Z
dc.date.available2018-07-02T17:08:19Z
dc.date.issued2016-04
dc.date.submitted2016-03
dc.identifier.issn1532-4435
dc.identifier.issn1533-7928
dc.identifier.urihttp://hdl.handle.net/1721.1/116717
dc.description.abstractThe proliferation of electronic health records (EHRs) frames opportunities for using machine learning to build models that help healthcare providers improve patient outcomes. However, building useful risk stratification models presents many technical challenges including the large number of factors (both intrinsic and extrinsic) influencing a patient's risk of an adverse outcome and the inherent evolution of that risk over time. We address these challenges in the context of learning a risk stratification model for predicting which patients are at risk of acquiring a Clostridium difficile infection (CDI). We take a novel data-centric approach, leveraging the contents of EHRs from nearly 50,000 hospital admissions. We show how, by adapting techniques from multitask learning, we can learn models for patient risk stratification with unprecedented classification performance. Our model, based on thousands of variables, both time-varying and time-invariant, changes over the course of a patient admission. Applied to a held out set of approximately 25,000 patient admissions, we achieve an area under the receiver operating characteristic curve of 0.81 (95% CI 0.78-0.84). The model has been integrated into the health record system at a large hospital in the US, and can be used to produce daily risk estimates for each inpatient. While more complex than traditional risk stratification methods, the widespread development and use of such data-driven models could ultimately enable cost-effective, targeted prevention strategies that lead to better patient outcomes.en_US
dc.publisherJMLR, Inc.en_US
dc.relation.isversionofhttp://dx.doi.org/en_US
dc.rightsArticle is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.en_US
dc.sourceJournal of Machine Learning Researchen_US
dc.titlePatient risk stratification with time-varying parameters: A multitask learning approachen_US
dc.typeArticleen_US
dc.identifier.citationWiens, Jenna, John Guttag and Eric Horvitz. "Patient risk stratification with time-varying parameters: A multitask learning approach." Journal of Machine Learning Research, 17 (2016): 1-23.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratoryen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.contributor.mitauthorWiens, Jenna Anne Marleau
dc.contributor.mitauthorGuttag, John V
dc.relation.journalJournal of Machine Learning Researchen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2018-02-23T16:07:06Z
dspace.orderedauthorsWiens, Jenna; Guttag, John; Horvitz, Ericen_US
dspace.embargo.termsNen_US
dc.identifier.orcidhttps://orcid.org/0000-0003-0992-0906
mit.licensePUBLISHER_POLICYen_US


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