Interpretable Machine Learning Model for Pulmonary Hypertension Risk Prediction: Retrospective Cohort Study
This model enhances early PH diagnosis through a noninvasive approach and demonstrates strong predictive accuracy. It facilitates early intervention and personalized treatment, with potential applications in broader cardiovascular disease management.
Authors
- Xiaojun Hao
- Zhenshun Cheng (ORCID: https://orcid.org/0000-0002-7387-496X)
- Hongxia Jiang (ORCID: https://orcid.org/0000-0003-4575-2671)
- H. Gao (ORCID: https://orcid.org/0009-0006-4207-0818)
Institutions
- Wuhan University (CN)
- Zhongnan Hospital of Wuhan University (CN)
- Wuhan Center for Disease Control and Prevention (CN)
Publication Details
- Journal
- JMIR Medical Informatics
- Published
- 2025-08-15
- DOI
- https://doi.org/10.2196/74117
- Citations
- 1
- Primary Topic
- Pulmonary Hypertension Research and Treatments
- Type
- article
- Field-Weighted Citation Impact
- 1.52