Machine learning enhanced acute heart failure phenotype prediction using natural language processing and random forest
Our study enhances HF phenotype classification and underscores the value of multifaceted data analysis in clinical informatics, enabling more personalized heart failure treatment. Early identification of AHF phenotypes may support timely, phenotype-specific management and inform treatment decisions.
Authors
- Fang-Ju Sun (ORCID: https://orcid.org/0009-0008-5070-5320)
- Pei-Hsuan Chang
- Y. Liu (ORCID: https://orcid.org/0000-0002-8374-3947)
- Chung-Lieh Hung
- Hung‐I Yeh (ORCID: https://orcid.org/0000-0003-3206-0908)
- Kun‐Pin Wu
- Yi-Ching Wu
- Feng‐Ching Liao
Institutions
- National Yang Ming Chiao Tung University (TW)
- Mackay Memorial Hospital (TW)
- Mackay Medical University (TW)
Publication Details
- Journal
- Frontiers in Artificial Intelligence
- Published
- 2025-10-16
- DOI
- https://doi.org/10.3389/frai.2025.1664627
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Science Council
- Mackay Memorial Hospital
- National Science and Technology Council