000			04225cam a2200565 i 4500
001			20987519
005			20230518101752.0
008			190528s2020 flua b 001 0 eng c
010			_a 2019941841
016	7		_a101763352 _2DNLM
020			_a9780367342906 _q(hardback ; _qalk. paper)
020			_a0367342901 _q(hardback ; _qalk. paper)
035			_a(OCoLC)on1102647135
040			_aNLM _beng _cNLM _erda _dYDXIT _dOCLCF _dNUI _dYDX _dOCLCO _dOCLCQ _dOCLCA _dUPM _dOCLCO _dDLC
042			_apcc
043			_an-us---
050	0	0	_aRA410.6 _b.Y36 2020
060	0	0	_aW 86
082			_a362.1068 _bYAN
100	1		_aYang, Chengliang _c(Of University of Florida), _eauthor. _9112416
245	1	0	_aData-driven approaches for health care : _bmachine learning for identifying high utilizers / _cChengliang Yang, Chris Delcher, Elizabeth Shenkman, Sanjay Ranka.
264		1	_aBoca Raton : _bCRC Press, _c[2020]
300			_aix, 107 pages : _billustrations ; _c26 cm
336			_atext _btxt _2rdacontent
337			_aunmediated _bn _2rdamedia
338			_avolume _bnc _2rdacarrier
490	1		_aChapman & Hall/CRC big data series
500			_a"A Chapman & Hall book."
500			_aIntroduction. Overview of Healthcare Data. Machine Learning Modeling from Healthcare Data. Machine Learning Modeling from Healthcare Data. Descriptive Analysis of High Utilizers. Residuals Analysis for Identifying High Utilizers. Machine Learning Results for High Utilizers.
504			_aIncludes bibliographical references and index.
505	0		_aIntroduction. Overview of Healthcare Data. Machine Learning Modeling from Healthcare Data. Machine Learning Modeling from Healthcare Data. Descriptive Analysis of High Utlizers. Residuals Analysis for Identifying High Utilizers. Machine Learning Results for High Utilizers.
520			_aHealth 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. Key Features: Introduces basic elements of health care data, especially for administrative claims data, including disease code, procedure codes, and drug codes Provides tailored supervised and unsupervised machine learning approaches for understanding and predicting the high utilizers Presents descriptive data driven methods for the high utilizer population Identifies a best-fitting linear and tree-based regression model to account for patients' acute and chronic condition loads and demographic characteristics.-- _cSource other than the Library of Congress.
650		0	_aMedical care _xUtilization _xMathematical models. _9112417
650		0	_aMachine learning.
650	1	2	_aMedical Overuse _xprevention & control _9112418
650	2	2	_aMedical Overuse _xstatistics & numerical data _9112419
650	2	2	_aModels, Theoretical _92968
650	2	2	_aMachine Learning
650		7	_aMachine learning. _2fast _0(OCoLC)fst01004795 _9112420
650		7	_aMedical care _xUtilization _xMathematical models. _2fast _0(OCoLC)fst01013885 _9112417
651		2	_aUnited States
700	1		_aDelcher, Chris, _eauthor. _9112421
700	1		_aShenkman, Elizabeth, _eauthor. _9112422
700	1		_aRanka, Sanjay, _eauthor. _9101224
776	0	8	_iElectronic version: _aYang, Chengliang. _tData driven approaches for healthcare. _dBoca Raton : CRC Press, Taylor & Francis Group, 2020 _z9780429342769 _w(OCoLC)1121596821
830		0	_aChapman & Hall/CRC big data series. _9112423
906			_a7 _bcbc _cpccadap _d2 _encip _f20 _gy-gencatlg
942			_2ddc _cBK
999			_c594800 _d594800