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Data Driven Approaches for Healthcare - Machine learning for Identifying High Utilizers (Paperback): Chengliang Yang, Chris... Data Driven Approaches for Healthcare - Machine learning for Identifying High Utilizers (Paperback)
Chengliang Yang, Chris Delcher, Elizabeth Shenkman, Sanjay Ranka
R1,509 Discovery Miles 15 090 Ships in 12 - 19 working days

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

Data Driven Approaches for Healthcare - Machine learning for Identifying High Utilizers (Hardcover): Chengliang Yang, Chris... Data Driven Approaches for Healthcare - Machine learning for Identifying High Utilizers (Hardcover)
Chengliang Yang, Chris Delcher, Elizabeth Shenkman, Sanjay Ranka
R4,914 Discovery Miles 49 140 Ships in 12 - 19 working days

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

Opioid Prescribing Rates and Criminal Justice and Health Outcomes (Paperback, 1st ed. 2020): Wesley G. Jennings, Nicholas... Opioid Prescribing Rates and Criminal Justice and Health Outcomes (Paperback, 1st ed. 2020)
Wesley G. Jennings, Nicholas Perez, Chris Delcher, Yanning Wang
R1,521 Discovery Miles 15 210 Ships in 10 - 15 working days

This brief uses California's CURES (Controlled Substance Utilization Review and Evaluation System) 2.0 data to analyze county-level opioid prescribing rates in California from 2012 to 2017 from multiple perspectives. The book summarizes California's county-level opioid prescribing trends, examines potential correlates of opioid prescribing rates, and assesses the association of opioid prescribing on both criminal justice and public health outcomes. Finally, the authors discuss their principal findings and the implications for policy and practice, including the significant and lasting consequences of the opioid crisis on the criminal justice system and the importance of a multi-disciplinary approach to effectively address the crisis.

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