{"product_id":"data-driven-approaches-for-healthcare-machine-learning-for-identifying-high-utilizers-paperback-1","title":"Data Driven Approaches for Healthcare: Machine Learning for Identifying High Utilizers - Paperback","description":"\u003cdiv\u003e\u003cp style=\"text-align: right;\"\u003e\u003ca href=\"https:\/\/reportcopyrightinfringement.com\/\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cb\u003eReport copyright infringement\u003c\/b\u003e\u003c\/a\u003e\u003c\/p\u003e\u003c\/div\u003e\u003cp\u003eby \u003cb\u003eChengliang Yang\u003c\/b\u003e (Author), \u003cb\u003eChris Delcher\u003c\/b\u003e (Author), \u003cb\u003eElizabeth Shenkman\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eHealth care utilization routinely generates vast amounts of data from sources ranging from electronic medical records, insurance claims, vital signs, and patient-reported outcomes. Predicting health outcomes using data modeling approaches is an emerging field that can reveal important insights into disproportionate spending patterns. This book presents data driven methods, especially machine learning, for understanding and approaching the high utilizers problem, using the example of a large public insurance program. It describes important goals for data driven approaches from different aspects of the high utilizer problem, and identifies challenges uniquely posed by this problem.\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eKey Features: \u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cli\u003eIntroduces basic elements of health care data, especially for administrative claims data, including disease code, procedure codes, and drug codes\u003c\/li\u003e\u003cp\u003e\u003c\/p\u003e\u003cli\u003eProvides tailored supervised and unsupervised machine learning approaches for understanding and predicting the high utilizers\u003c\/li\u003e\u003cp\u003e\u003c\/p\u003e\u003cli\u003ePresents descriptive data driven methods for the high utilizer population\u003c\/li\u003e\u003cp\u003e\u003c\/p\u003e\u003cli\u003eIdentifies a best-fitting linear and tree-based regression model to account for patients' acute and chronic condition loads and demographic characteristics\u003c\/li\u003e\u003ch3\u003eAuthor Biography\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eChengliang Yang\u003c\/strong\u003e, Department of Computer Science, University of Florida Chris Delcher, Institute of Child Health Policy, University of Florida Elizabeth Shenkman, Institute of Child Health Policy, University of Florida Sanjay Ranka, Department of Computer Science, University of Florida.\u003c\/p\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 120\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 0.25 x 10 x 7 IN\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e June 30, 2021\u003c\/div\u003e\n            ","brand":"BooksCloud","offers":[{"title":"Default Title","offer_id":47204903846137,"sku":"9781032088686","price":108.52,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0789\/2782\/3097\/files\/N3dtZlh1NHlsUldhS1REZUhDck12QT09_e1b8d6f6-3609-4005-8519-f3deecc319ec.webp?v=1768022541","url":"https:\/\/bookscloud.io\/products\/data-driven-approaches-for-healthcare-machine-learning-for-identifying-high-utilizers-paperback-1","provider":"BooksCloud Book Dropshipping","version":"1.0","type":"link"}