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001			9780429342769
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005			20220531132314.0
006			m o d
007			cr cnu|||unuuu
008			191003s2020 flu ob 001 0 eng d
040			_aOCoLC-P _beng _erda _epn _cOCoLC-P
020			_a9780429342769 _q(electronic bk.)
020			_a0429342764 _q(electronic bk.)
020			_a9781000700039 _q(electronic bk. : PDF)
020			_a1000700038 _q(electronic bk. : PDF)
020			_z9780367342906
020			_a9781000701258 _q(electronic bk. : EPUB)
020			_a1000701255 _q(electronic bk. : EPUB)
020			_a9781000700640 _q(electronic bk. : Mobipocket)
020			_a100070064X _q(electronic bk. : Mobipocket)
035			_a(OCoLC)1121596821
035			_a(OCoLC-P)1121596821
050		4	_aRA410.6
072		7	_aBUS _x070080 _2bisacsh
072		7	_aCOM _x000000 _2bisacsh
072		7	_aCOM _x012040 _2bisacsh
072		7	_aUY _2bicssc
082	0	4	_a362.1068/3 _223
100	1		_aYang, Chengliang, _eauthor.
245	1	0	_aData driven approaches for healthcare : _bmachine learning for identifying high utilizers / _cChengliang Yang, Chris Delcher, Elizabeth Shenkman, Sanjay Ranka.
264		1	_aBoca Raton : _bCRC Press, Taylor & Francis Group, _c2020.
300			_a1 online resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
490	1		_aChapman & Hall/CRC big data series
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
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.
588			_aOCLC-licensed vendor bibliographic record.
650		0	_aMedical care _xUtilization _xMathematical models.
650		0	_aMachine learning.
650		7	_aBUSINESS & ECONOMICS / Industries / Service Industries _2bisacsh
650		7	_aCOMPUTERS / General _2bisacsh
650		7	_aCOMPUTERS / Computer Graphics / Game Programming & Design _2bisacsh
700	1		_aDelcher, Chris, _eauthor.
700	1		_aShenkman, Elizabeth, _eauthor.
700	1		_aRanka, Sanjay, _eauthor.
856	4	0	_3Taylor & Francis _uhttps://www.taylorfrancis.com/books/9780429342769
856	4	2	_3OCLC metadata license agreement _uhttp://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf
999			_c70535 _d70535