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Data Driven Approaches for Healthcare

Chengliang Yang | Chris Delcher | Elizabeth Shenkman | Sanjay Ranka
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A01=Chengliang Yang
A01=Chris Delcher
A01=Elizabeth Shenkman
A01=Sanjay Ranka
administrative claims analysis
Administrative Claims Data
Annualize ED Visit
Author_Chengliang Yang
Author_Chris Delcher
Author_Elizabeth Shenkman
Author_Sanjay Ranka
Category=UBH
Category=UN
Category=UYQM
Chronic Kidney Disease Cohort
Clinical Classifications Software
data science
ED Utilization
ED Visit
eq_bestseller
eq_computing
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
Health Care Data
Health Care Data Systems
Health Care Service Usage
healthcare analytics
High ED
Hospital Inpatient Visits
ICD-10 Cm Code
ICD-9 Cm Diagnosis Code
machine learning
Methicillin Susceptible Staphylococcus Aureus
Object Image Classification
patient risk stratification
Predict Health Care Utilization
predictive modeling
predictive modeling for high cost patients
regression modeling healthcare
Spectral Clustering
Sud Prevalence
supervised learning
supervised learning methods
Supervised Machine Learning
Texas Medicaid
Total ED
Total ED Visit
Tree Based Regression Model
unsupervised clustering techniques
unsupervised learning
Unsupervised Machine Learning
Wasserstein Distance

Product details

  • ISBN 9781032088686
  • Weight: 222g
  • Dimensions: 178 x 254mm
  • Publication Date: 30 Jun 2021
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Paperback
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Description

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
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    About Chengliang Yang | Chris Delcher | Elizabeth Shenkman | Sanjay Ranka

    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.

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