Prediction of hospitalization due to heart diseases by supervised learning methods. Issue 3 (March 2015)

• Record Type: Journal Article
• Authors: Dai, Wuyang; Brisimi, Theodora S.; Adams, William G.; Mela, Theofanie; Saligrama, Venkatesh; Paschalidis, Ioannis Ch.
• Is Part Of: International journal of medical informatics. Volume 84:Issue 3(2015:Mar.)
• Page Start: 189
• Page End: 197
• Publication Date: 2015-03
• Subjects: Medical informatics -- Periodicals; Information science -- Periodicals; Computers -- Periodicals; Medical technology -- Periodicals; Medical Informatics -- Periodicals; Technology, Medical -- Periodicals; Computers; Information science; Medical informatics; Medical technology; Electronic journals; Periodicals; Electronic journals; 610.285
• DOI: 10.1016/j.ijmedinf.2014.10.002
• Languages: English
• ISSNs: 1386-5056

Abstract

<abstract abstract-type="author-highlights" id="abs0005"> <title id="sect0005">Highlights</title> <sec> <p id="spar0060"> <list id="lis0025"> <list-item id="lsti0075"> <label></label> <p id="par0380">Using the EHR, it is possible to accurately predict heart-related hospitalizations.</p> </list-item> <list-item id="lsti0080"> <label></label> <p id="par0385">Machine learning methods improve prediction accuracy over existing risk metrics.</p> </list-item> <list-item id="lsti0085"> <label></label> <p id="par0390">A novel Likelihood Ratio Test provides prediction accuracy and interpretability.</p> </list-item> <list-item id="lsti0090"> <label></label> <p id="par0395">Implementing such methods can lead to improved care and drastic cost savings.</p> </list-item> </list> </p> </sec> </abstract>