Development and validation of machine learning-derived frailty index in predicting outcomes of patients undergoing percutaneous coronary intervention
The resultant model developed using novel machine learning methodologies had good predictive power for significant clinical outcomes post PCI with potential to be automated within hospital information systems.
Authors
- Rodney Y.H. Soh
- Andie Hartanto Djohan (ORCID: https://orcid.org/0000-0001-9335-178X)
- Li Feng Tan (ORCID: https://orcid.org/0000-0003-1232-9308)
- Miaolin Feng (ORCID: https://orcid.org/0000-0003-2850-4254)
- Tiong‐Cheng Yeo (ORCID: https://orcid.org/0000-0002-2374-1069)
- John Soong (ORCID: https://orcid.org/0000-0003-4235-8505)
- Mark Y. Chan (ORCID: https://orcid.org/0000-0003-0389-7450)
- Hui Wen Sim (ORCID: https://orcid.org/0000-0003-2006-9454)
- Ching‐Hui Sia (ORCID: https://orcid.org/0000-0002-2764-2869)
- Wenbo He (ORCID: https://orcid.org/0000-0001-6243-1496)
- Han-Kim Tan
Institutions
- National University of Singapore (SG)
- Sichuan University (CN)
- Alexandra Hospital (SG)
- West China Hospital of Sichuan University (CN)
- National University Heart Centre Singapore (SG)
- National University Hospital (SG)
- National University Health System (SG)
Publication Details
- Journal
- IJC Heart & Vasculature
- Published
- 2024-09-21
- DOI
- https://doi.org/10.1016/j.ijcha.2024.101511
- Citations
- 2
- Primary Topic
- Frailty in Older Adults
- Type
- article
- Field-Weighted Citation Impact
- 0.69
Funders
- Cisco Systems
- National University of Singapore
- National University Health System
- National Medical Research Council