Data Driven Approaches for Healthcare Machine learning for Identifying High Utilizers, 1st edition
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...
Đã lưu trong:
| Những tác giả chính: | , , , |
|---|---|
| Định dạng: | Sách |
| Ngôn ngữ: | English |
| Được phát hành: |
Chapman and Hall/CRC
2020
|
| Những chủ đề: | |
| Truy cập trực tuyến: | https://scholar.dlu.edu.vn/thuvienso/handle/DLU123456789/93542 https://doi.org/10.1201/9780429342769 |
| Các nhãn: |
Thêm thẻ
Không có thẻ, Là người đầu tiên thẻ bản ghi này!
|
| Thư viện lưu trữ: | Thư viện Trường Đại học Đà Lạt |
|---|
| id |
oai:scholar.dlu.edu.vn:DLU123456789-93542 |
|---|---|
| record_format |
dspace |
| spelling |
oai:scholar.dlu.edu.vn:DLU123456789-935422023-11-11T07:05:23Z Data Driven Approaches for Healthcare Machine learning for Identifying High Utilizers, 1st edition Yang, Chengliang Delcher, Chris Shenkman, Elizabeth Ranka, Sanjay Computer science Economics Finance Information science 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 2020-06-03T03:09:14Z 2020-06-03T03:09:14Z 2019 Book 9780429342769 https://scholar.dlu.edu.vn/thuvienso/handle/DLU123456789/93542 https://doi.org/10.1201/9780429342769 en application/pdf Chapman and Hall/CRC New York |
| institution |
Thư viện Trường Đại học Đà Lạt |
| collection |
Thư viện số |
| language |
English |
| topic |
Computer science Economics Finance Information science |
| spellingShingle |
Computer science Economics Finance Information science Yang, Chengliang Delcher, Chris Shenkman, Elizabeth Ranka, Sanjay Data Driven Approaches for Healthcare Machine learning for Identifying High Utilizers, 1st edition |
| 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 |
| format |
Book |
| author |
Yang, Chengliang Delcher, Chris Shenkman, Elizabeth Ranka, Sanjay |
| author_facet |
Yang, Chengliang Delcher, Chris Shenkman, Elizabeth Ranka, Sanjay |
| author_sort |
Yang, Chengliang |
| title |
Data Driven Approaches for Healthcare
Machine learning for Identifying High Utilizers, 1st edition |
| title_short |
Data Driven Approaches for Healthcare
Machine learning for Identifying High Utilizers, 1st edition |
| title_full |
Data Driven Approaches for Healthcare
Machine learning for Identifying High Utilizers, 1st edition |
| title_fullStr |
Data Driven Approaches for Healthcare
Machine learning for Identifying High Utilizers, 1st edition |
| title_full_unstemmed |
Data Driven Approaches for Healthcare
Machine learning for Identifying High Utilizers, 1st edition |
| title_sort |
data driven approaches for healthcare
machine learning for identifying high utilizers, 1st edition |
| publisher |
Chapman and Hall/CRC |
| publishDate |
2020 |
| url |
https://scholar.dlu.edu.vn/thuvienso/handle/DLU123456789/93542 https://doi.org/10.1201/9780429342769 |
| _version_ |
1819791117374193664 |