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Clinical data mining for physician d...

Cerrito, John, (1954-)

Clinical data mining for physician decision making and investigating health outcomes[electronic resource] :methods for prediction and analysis /

レコード種別:	言語・文字資料 (印刷物) : 単行資料
[NT 15000414] null:	610.285
タイトル / 著者:	Clinical data mining for physician decision making and investigating health outcomes : methods for prediction and analysis // Patricia Cerrito, John Cerrito.
その他の著者:	Cerrito, John,
出版された:	Hershey, Pa. : : IGI Global (701 E. Chocolate Avenue, Hershey, Pennsylvania, 17033, USA),, c2010.
記述:	1 online resource (351 p.) : : ill.
主題:	Medical informatics.
主題:	Data mining.
主題:	Evidence-based medicine - Data processing.
主題:	Data Mining.
主題:	Medical Informatics Computing.
主題:	Database Management Systems.
主題:	Decision Making.
主題:	Evidence-Based Medicine.
国際標準図書番号 (ISBN) :	9781615209064 (ebook)
国際標準図書番号 (ISBN) :	9781615209057 (hbk.)
[NT 15000227] null:	Includes bibliographical references.
[NT 15000228] null:	Preprocessing the data -- Errors and missing values in the dataset -- Introduction to the use of MEPS (medical expenditure panel survey) -- Preprocessing Medpar data -- Extracting data from the national inpatient sample -- Creating a one-to-one relationship in the data from a many-to-many -- Merging different datasets to allow for a complete analysis (inpatient, outpatient, physician visits, medications) -- Introduction to analysis using time components -- More survival data mining-multiple time of endpoints -- Using the data to define patient compliance -- Compression of diagnosis and procedure codes -- Comparisons of patient severity indices -- Decision trees and their development -- Example of diabetes using CMS data -- Example of breathing illnesses, asthma and COPD using MEPS data -- Example of wound care using Medpar data -- Discussion.
[NT 15000229] null:	The investigation of healthcare databases can be used to examine physician decisions and develop evidence-based treatment guidelines that optimize patient outcomes. This book demonstrates how concern for detail in datasets and the use of data mining techniques can extract important and meaningful knowledge from healthcare databases. Basic information on processing data with step-by-step instructions is provided, allowing readers to use their own data and follow the instructions to find meaningful results.
電子資源:	http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-61520-905-7

Clinical data mining for physician decision making and investigating health outcomes[electronic resource] :methods for prediction and analysis /
Clinical data mining for physician decision making and investigating health outcomesmethods for prediction and analysis /[electronic resource] :Patricia Cerrito, John Cerrito. - Hershey, Pa. :IGI Global (701 E. Chocolate Avenue, Hershey, Pennsylvania, 17033, USA),c2010. - 1 online resource (351 p.) :ill.

Includes bibliographical references.

Preprocessing the data -- Errors and missing values in the dataset -- Introduction to the use of MEPS (medical expenditure panel survey) -- Preprocessing Medpar data -- Extracting data from the national inpatient sample -- Creating a one-to-one relationship in the data from a many-to-many -- Merging different datasets to allow for a complete analysis (inpatient, outpatient, physician visits, medications) -- Introduction to analysis using time components -- More survival data mining-multiple time of endpoints -- Using the data to define patient compliance -- Compression of diagnosis and procedure codes -- Comparisons of patient severity indices -- Decision trees and their development -- Example of diabetes using CMS data -- Example of breathing illnesses, asthma and COPD using MEPS data -- Example of wound care using Medpar data -- Discussion.

Restricted to subscribers or individual electronic text purchasers.

The investigation of healthcare databases can be used to examine physician decisions and develop evidence-based treatment guidelines that optimize patient outcomes. This book demonstrates how concern for detail in datasets and the use of data mining techniques can extract important and meaningful knowledge from healthcare databases. Basic information on processing data with step-by-step instructions is provided, allowing readers to use their own data and follow the instructions to find meaningful results.

Mode of access: World Wide Web.

ISBN: 9781615209064 (ebook)

LCCN: 2010006306Subjects--Topical Terms:

340360
Medical informatics.

LC Class. No.: R859.7.D36 / C55 2010e

Dewey Class. No.: 610.285

National Library of Medicine Call No.: W 26.55.I4 / C6415 2010e

Clinical data mining for physician decision making and investigating health outcomes[electronic resource] :methods for prediction and analysis /
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010 $a 2010006306
020 $a 9781615209064 (ebook)
020 $a 9781615209057 (hbk.)
035 $a (CaBNVSL)gtp00543782
035 $a (OCoLC)649282549
035 $a 00000166
040 $a CaBNVSL $c CaBNVSL $d CaBNVSL
050 4 $a R859.7.D36 $b C55 2010e
060 4 $a W 26.55.I4 $b C6415 2010e
082 0 4 $a 610.285 $2 22
245 0 0 $a Clinical data mining for physician decision making and investigating health outcomes $h [electronic resource] : $b methods for prediction and analysis / $c Patricia Cerrito, John Cerrito.
260 $a Hershey, Pa. : $b IGI Global (701 E. Chocolate Avenue, Hershey, Pennsylvania, 17033, USA), $c c2010.
300 $a 1 online resource (351 p.) : $b ill.
504 $a Includes bibliographical references.
505 0 $a Preprocessing the data -- Errors and missing values in the dataset -- Introduction to the use of MEPS (medical expenditure panel survey) -- Preprocessing Medpar data -- Extracting data from the national inpatient sample -- Creating a one-to-one relationship in the data from a many-to-many -- Merging different datasets to allow for a complete analysis (inpatient, outpatient, physician visits, medications) -- Introduction to analysis using time components -- More survival data mining-multiple time of endpoints -- Using the data to define patient compliance -- Compression of diagnosis and procedure codes -- Comparisons of patient severity indices -- Decision trees and their development -- Example of diabetes using CMS data -- Example of breathing illnesses, asthma and COPD using MEPS data -- Example of wound care using Medpar data -- Discussion.
506 $a Restricted to subscribers or individual electronic text purchasers.
520 3 $a The investigation of healthcare databases can be used to examine physician decisions and develop evidence-based treatment guidelines that optimize patient outcomes. This book demonstrates how concern for detail in datasets and the use of data mining techniques can extract important and meaningful knowledge from healthcare databases. Basic information on processing data with step-by-step instructions is provided, allowing readers to use their own data and follow the instructions to find meaningful results.
530 $a Also available in print.
538 $a Mode of access: World Wide Web.
650 0 $a Medical informatics. $3 340360
650 0 $a Data mining. $3 337740
650 0 $a Evidence-based medicine $x Data processing. $3 571017
650 1 2 $a Data Mining. $3 559047
650 1 2 $a Medical Informatics Computing. $3 428045
650 2 2 $a Database Management Systems. $3 571018
650 2 2 $a Decision Making. $3 193156
650 2 2 $a Evidence-Based Medicine. $3 341836
700 1 $a Cerrito, John, $d 1954- $3 571016
700 1 $a Cerrito, Patricia B. $3 389475
856 4 8 $3 Chapter PDFs via platform: $u http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-61520-905-7

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