Exploring Mortality and Prognostic Factors of Heart Failure with In-Hospital and Emergency Patients by Electronic Medical Records: A Machine Learning Approach
Exploring HF mortality and its patterns related to clinical risk factors by machine learning models can help physicians make appropriate decisions when monitoring HF patients' health quality in the hospital.
Authors
- Tzu‐Hao Chang (ORCID: https://orcid.org/0000-0001-7011-3754)
- Jenny L Wu (ORCID: https://orcid.org/0000-0002-5589-6428)
- Yu-Da Chen
- Cheng-Sheng Yu (ORCID: https://orcid.org/0000-0002-3334-0816)
- Kuan-Lin Chiu
- Chun‐Ming Shih (ORCID: https://orcid.org/0000-0002-5621-7108)
Institutions
- MetLife (United States) (US)
- Taipei Medical University Hospital (TW)
- Taipei Medical University (TW)
Publication Details
- Journal
- Risk Management and Healthcare Policy
- Published
- 2025-01-01
- DOI
- https://doi.org/10.2147/rmhp.s488159
- Citations
- 4
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 9.92
Funders
- Ministry of Education, India
- National Science and Technology Council