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dc.contributor.authorSingh, Anima
dc.contributor.authorNadkarni, Girish
dc.contributor.authorGottesman, Omri
dc.contributor.authorEllis, Stephen B.
dc.contributor.authorBottinger, Erwin P.
dc.contributor.authorGuttag, John V.
dc.date.accessioned2016-02-09T16:11:32Z
dc.date.available2016-02-09T16:11:32Z
dc.date.issued2014-11
dc.date.submitted2014-06
dc.identifier.issn15320464
dc.identifier.issn1532-0480
dc.identifier.urihttp://hdl.handle.net/1721.1/101133
dc.description.abstractPredictive models built using temporal data in electronic health records (EHRs) can potentially play a major role in improving management of chronic diseases. However, these data present a multitude of technical challenges, including irregular sampling of data and varying length of available patient history. In this paper, we describe and evaluate three different approaches that use machine learning to build predictive models using temporal EHR data of a patient. The first approach is a commonly used non-temporal approach that aggregates values of the predictors in the patient’s medical history. The other two approaches exploit the temporal dynamics of the data. The two temporal approaches vary in how they model temporal information and handle missing data. Using data from the EHR of Mount Sinai Medical Center, we learned and evaluated the models in the context of predicting loss of estimated glomerular filtration rate (eGFR), the most common assessment of kidney function. Our results show that incorporating temporal information in patient’s medical history can lead to better prediction of loss of kidney function. They also demonstrate that exactly how this information is incorporated is important. In particular, our results demonstrate that the relative importance of different predictors varies over time, and that using multi-task learning to account for this is an appropriate way to robustly capture the temporal dynamics in EHR data. Using a case study, we also demonstrate how the multi-task learning based model can yield predictive models with better performance for identifying patients at high risk of short-term loss of kidney function.en_US
dc.language.isoen_US
dc.publisherElsevieren_US
dc.relation.isversionofhttp://dx.doi.org/10.1016/j.jbi.2014.11.005en_US
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivs Licenseen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/en_US
dc.sourcePMCen_US
dc.titleIncorporating temporal EHR data in predictive models for risk stratification of renal function deteriorationen_US
dc.typeArticleen_US
dc.identifier.citationSingh, Anima, Girish Nadkarni, Omri Gottesman, Stephen B. Ellis, Erwin P. Bottinger, and John V. Guttag. “Incorporating Temporal EHR Data in Predictive Models for Risk Stratification of Renal Function Deterioration.” Journal of Biomedical Informatics 53 (February 2015): 220–228.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.mitauthorSingh, Animaen_US
dc.contributor.mitauthorGuttag, John V.en_US
dc.relation.journalJournal of Biomedical Informaticsen_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dspace.orderedauthorsSingh, Anima; Nadkarni, Girish; Gottesman, Omri; Ellis, Stephen B.; Bottinger, Erwin P.; Guttag, John V.en_US
dc.identifier.orcidhttps://orcid.org/0000-0003-0992-0906
mit.licensePUBLISHER_CCen_US


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