High-Throughput Machine Learning from Electronic Health Records

Autor: Kleiman, Ross S., Bennett, Paul S., Peissig, Peggy L., Berg, Richard L., Kuang, Zhaobin, Hebbring, Scott J., Caldwell, Michael D., Page, David

The widespread digitization of patient data via electronic health records (EHRs) has created an unprecedented opportunity to use machine learning algorithms to better predict disease risk at the patient level. Although predictive models have previous

Externí odkaz: http://arxiv.org/abs/1907.01901

Using Machine Learning Algorithms to Predict Risk for Development of Calciphylaxis in Patients with Chronic Kidney Disease.

Autor: Kleiman RS; Marshfield Clinic Research Institute, Marshfield, WI.; Department of Computer Sciences, University of Wisconsin, Madison, WI., LaRose ER; Marshfield Clinic Research Institute, Marshfield, WI., Badger JC; Marshfield Clinic Research Institute, Marshfield, WI.; Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, WI., Page D; Department of Computer Sciences, University of Wisconsin, Madison, WI.; Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, WI., Caldwell MD; Center for Hyperbaric Medicine and Tissue Repair, Marshfield Clinic., Clay JA; Department of Surgery, Marshfield Clinic., Peissig PL; Marshfield Clinic Research Institute, Marshfield, WI.

Publikováno v: AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science [AMIA Jt Summits Transl Sci Proc] 2018 May 18; Vol. 2017, pp. 139-146. Date of Electronic Publication: 2018 May 18 (Print Publication: 2018).