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Data Driven Approaches for Healthcare Machine learning for Identifying High Utilizers

Yang, Chengliang Delcher, Chris Shenkman, Elizabeth Se alle
Heftet / 2021 / Engelsk

Produktdetaljer

ISBN
9781032088686
Publisert
2021-06-30
Utgiver
Vendor
Chapman & Hall/CRC
Vekt
222 gr
Høyde
254 mm
Bredde
178 mm
Aldersnivå
U, G, 05, 01
Språk
Product language
Engelsk
Format
Product format
Heftet
Antall sider
120

Forfatter
Yang, Chengliang
Delcher, Chris
Shenkman, Elizabeth
Ranka Sanjay

Om bidragsyterne

Chengliang Yang, Department of Computer Science, University of Florida Chris Delcher, Institute of Child Health Policy, University of Florida Elizabeth Shenkman, Institute of Child Health Policy, University of Florida Sanjay Ranka, Department of Computer Science, University of Florida.

Data Driven Approaches for Healthcare Machine learning for Identifying High Utilizers

Yang, Chengliang Delcher, Chris Shenkman, Elizabeth Se alle
Heftet / 2021 / Engelsk
Yang, Chengliang Delcher, Chris Shenkman, Elizabeth Se alle
Heftet / 2021 / Engelsk
Nettpris:
714,-
Levering 7-20 dager
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Health 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
    • Les mer
    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 posed by this problem.
    Les mer
    Introduction. 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.
    Les mer

    Relaterte produkter

    Health 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
    • Les mer
    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 posed by this problem.
    Les mer
    Introduction. 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.
    Les mer

    Produktdetaljer

    ISBN
    9781032088686
    Publisert
    2021-06-30
    Utgiver
    Vendor
    Chapman & Hall/CRC
    Vekt
    222 gr
    Høyde
    254 mm
    Bredde
    178 mm
    Aldersnivå
    U, G, 05, 01
    Språk
    Product language
    Engelsk
    Format
    Product format
    Heftet
    Antall sider
    120

    Forfatter
    Yang, Chengliang
    Delcher, Chris
    Shenkman, Elizabeth
    Ranka Sanjay

    Om bidragsyterne

    Chengliang Yang, Department of Computer Science, University of Florida Chris Delcher, Institute of Child Health Policy, University of Florida Elizabeth Shenkman, Institute of Child Health Policy, University of Florida Sanjay Ranka, Department of Computer Science, University of Florida.

    Relaterte produkter