The 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 strati\ufb01cation models presents many technical challenges including the large number of factors (both intrinsic and extrinsic) in\ufb02uencing a patient\u2019s 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 strati\ufb01cation model for predicting which patients are at risk of acquiring a Clostridium di\ufb03cile 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 strati\ufb01cation with unprecedented classi\ufb01cation 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 strati\ufb01cation methods, the widespread development and use of such data-driven models could ultimately enable cost-e\ufb00ective, targeted prevention strategies that lead to better patient outcomes.<\/p>\n","protected":false},"excerpt":{"rendered":"
The 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 strati\ufb01cation models presents many technical challenges including the large number of factors (both intrinsic and extrinsic) in\ufb02uencing a patient\u2019s risk of an adverse outcome and the inherent evolution […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"Journal of Machine Learning Research LEHD (2016)","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"Journal of Machine Learning Research LEHD 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