000 | 03724cam a2200589Ii 4500 | ||
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001 | 9780429342769 | ||
003 | FlBoTFG | ||
005 | 20220724194151.0 | ||
006 | m o d | ||
007 | cr cnu|||unuuu | ||
008 | 191003s2020 flu ob 001 0 eng d | ||
040 |
_aOCoLC-P _beng _erda _epn _cOCoLC-P |
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020 |
_a9780429342769 _q(electronic bk.) |
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020 |
_a0429342764 _q(electronic bk.) |
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020 |
_a9781000700039 _q(electronic bk. : PDF) |
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020 |
_a1000700038 _q(electronic bk. : PDF) |
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020 | _z9780367342906 | ||
020 |
_a9781000701258 _q(electronic bk. : EPUB) |
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020 |
_a1000701255 _q(electronic bk. : EPUB) |
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020 |
_a9781000700640 _q(electronic bk. : Mobipocket) |
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020 |
_a100070064X _q(electronic bk. : Mobipocket) |
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035 | _a(OCoLC)1121596821 | ||
035 | _a(OCoLC-P)1121596821 | ||
050 | 4 | _aRA410.6 | |
072 | 7 |
_aBUS _x070080 _2bisacsh |
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_aUY _2bicssc |
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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. |
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300 | _a1 online resource. | ||
336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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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. |
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650 | 0 | _aMachine learning. | |
650 | 7 |
_aBUSINESS & ECONOMICS / Industries / Service Industries _2bisacsh |
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650 | 7 |
_aCOMPUTERS / General _2bisacsh |
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650 | 7 |
_aCOMPUTERS / Computer Graphics / Game Programming & Design _2bisacsh |
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700 | 1 |
_aDelcher, Chris, _eauthor. |
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700 | 1 |
_aShenkman, Elizabeth, _eauthor. |
|
700 | 1 |
_aRanka, Sanjay, _eauthor. |
|
856 | 4 | 0 |
_3Read Online _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 |
942 |
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999 |
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