Predictive modeling in urgent care: a comparative study of machine learning approaches
Abstract
The growing availability of rich clinical data such as patients’ electronic health records provide great opportunities to address a broad range of real-world questions in medicine. At the same time, artificial intelligence and machine learning (ML)-based approaches have shown great premise on extracting insights from those data and helping with various clinical problems. The goal of this study is to conduct a systematic comparative study of different ML algorithms for several predictive modeling problems in urgent care.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jun 04, 2018
- Source ID
- 10.1093/jamiaopen/ooy011
Entities
People
- Cao Xiao
- Fei Wang
- Fengyi Tang
- Jiayu Zhou
Organizations
- Cornell University
- IBM Research
- Michigan State University
- National Science Foundation
- Office of Naval Research