Clinical data mining for physician decision making and investigating health outcomes: methods for prediction and analysis
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 kn...
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Hershey ; New York
Medical Information Science Reference
[2010]
|
| Schriftenreihe: | Premier reference source
|
| Schlagworte: | |
| Online-Zugang: | DE-706 DE-1049 DE-898 DE-1050 DE-83 Volltext |
| Zusammenfassung: | 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 |
| Beschreibung: | Includes bibliographical references |
| Beschreibung: | 1 Online-Ressource (xiv, 356 Seiten) |
| ISBN: | 9781615209064 |
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| 505 | 8 | |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 | |
| 520 | |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 | ||
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| 650 | 4 | |a Data mining | |
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Datensatz im Suchindex
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| author | Cerrito, Patricia Cerrito, John |
| author_facet | Cerrito, Patricia Cerrito, John |
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| author_sort | Cerrito, Patricia |
| author_variant | p c pc j c jc |
| building | Verbundindex |
| bvnumber | BV044263035 |
| collection | ZDB-98-IGB ZDB-1-IGE |
| contents | 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 |
| ctrlnum | (OCoLC)992532233 (DE-599)BVBBV044263035 |
| format | Electronic eBook |
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| id | DE-604.BV044263035 |
| illustrated | Not Illustrated |
| indexdate | 2024-08-23T01:06:30Z |
| institution | BVB |
| isbn | 9781615209064 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-029667903 |
| oclc_num | 992532233 |
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| physical | 1 Online-Ressource (xiv, 356 Seiten) |
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| publishDate | 2010 |
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| publisher | Medical Information Science Reference |
| record_format | marc |
| series2 | Premier reference source |
| spelling | Cerrito, Patricia Verfasser aut Clinical data mining for physician decision making and investigating health outcomes methods for prediction and analysis Patricia Cerrito (University of Louisville, USA), John Cerrito (Kroger Pharmacy, USA) Hershey ; New York Medical Information Science Reference [2010] © 2010 1 Online-Ressource (xiv, 356 Seiten) txt rdacontent c rdamedia cr rdacarrier Premier reference source 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 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 Datenverarbeitung Medical informatics Data mining Evidence-based medicine / Data processing Data Mining (DE-588)4428654-5 gnd rswk-swf Medizinische Informatik (DE-588)4038261-8 gnd rswk-swf Data Mining (DE-588)4428654-5 s Medizinische Informatik (DE-588)4038261-8 s DE-604 Cerrito, John Verfasser aut Erscheint auch als Druck-Ausgabe 978-1-61520-905-7 Erscheint auch als Druck-Ausgabe 1-61520-905-0 Erscheint auch als Druck-Ausgabe 978-1-61692-336-5 http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-61520-905-7 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Cerrito, Patricia Cerrito, John Clinical data mining for physician decision making and investigating health outcomes methods for prediction and analysis 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 Datenverarbeitung Medical informatics Data mining Evidence-based medicine / Data processing Data Mining (DE-588)4428654-5 gnd Medizinische Informatik (DE-588)4038261-8 gnd |
| subject_GND | (DE-588)4428654-5 (DE-588)4038261-8 |
| title | Clinical data mining for physician decision making and investigating health outcomes methods for prediction and analysis |
| title_auth | Clinical data mining for physician decision making and investigating health outcomes methods for prediction and analysis |
| title_exact_search | Clinical data mining for physician decision making and investigating health outcomes methods for prediction and analysis |
| title_full | Clinical data mining for physician decision making and investigating health outcomes methods for prediction and analysis Patricia Cerrito (University of Louisville, USA), John Cerrito (Kroger Pharmacy, USA) |
| title_fullStr | Clinical data mining for physician decision making and investigating health outcomes methods for prediction and analysis Patricia Cerrito (University of Louisville, USA), John Cerrito (Kroger Pharmacy, USA) |
| title_full_unstemmed | Clinical data mining for physician decision making and investigating health outcomes methods for prediction and analysis Patricia Cerrito (University of Louisville, USA), John Cerrito (Kroger Pharmacy, USA) |
| title_short | Clinical data mining for physician decision making and investigating health outcomes |
| title_sort | clinical data mining for physician decision making and investigating health outcomes methods for prediction and analysis |
| title_sub | methods for prediction and analysis |
| topic | Datenverarbeitung Medical informatics Data mining Evidence-based medicine / Data processing Data Mining (DE-588)4428654-5 gnd Medizinische Informatik (DE-588)4038261-8 gnd |
| topic_facet | Datenverarbeitung Medical informatics Data mining Evidence-based medicine / Data processing Data Mining Medizinische Informatik |
| url | http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-61520-905-7 |
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