Machine learning algorithms for identifying tuberculous pericardial effusion: insights from a retrospective study in Vietnam
Machine learning algorithms, particularly the random tree model, demonstrate promising potential for improving TPE diagnosis through noninvasive data analysis. However, successful implementation requires external validation and careful consideration of local healthcare capabilities.
Authors
- Hai Tran (ORCID: https://orcid.org/0000-0001-9223-133X)
- V. Dang (ORCID: https://orcid.org/0009-0006-0039-0522)
- Kha Minh Nguyen (ORCID: https://orcid.org/0000-0002-8734-365X)
- Lan Thanh Phan (ORCID: https://orcid.org/0000-0002-1310-586X)
- Tai Nhat Nguyen
- Sang Quang Ly (ORCID: https://orcid.org/0009-0000-7644-2591)
- Nam Nhat Nguyen
- Sy Van Hoang
Institutions
- Ho Chi Minh City International University (VN)
- Cho Ray Hospital (VN)
- University of Medicine and Pharmacy at Ho Chi Minh City (VN)
- Pham Ngoc Thach Hospital (VN)
Publication Details
- Journal
- BMC Medical Informatics and Decision Making
- Published
- 2026-03-21
- DOI
- https://doi.org/10.1186/s12911-026-03454-9
- Primary Topic
- Pericarditis and Cardiac Tamponade
- Type
- article
- Field-Weighted Citation Impact
- 0.00