Machine learning for sudden cardiac death prediction among older adults using community-based electronic health records
ML models can significantly enhance the prediction of SCD risk using community-based EHRs. Our proposed risk model may enable the identification of high-risk individuals among older adults, facilitating targeted interventions and personalized care strategies. Future research should focus on integrating this model into routine primary care workflows and evaluating its effectiveness in real-world settings.
Authors
- Chunxia Cao (ORCID: https://orcid.org/0000-0003-4857-425X)
- Zihan Mei (ORCID: https://orcid.org/0000-0002-8552-0034)
- Haojun Fan (ORCID: https://orcid.org/0000-0003-1111-0232)
- Guolei Sun
- Marcus Eng Hock Ong (ORCID: https://orcid.org/0000-0001-7874-7612)
- Ji Li (ORCID: https://orcid.org/0000-0003-0931-5562)
- Yue Li (ORCID: https://orcid.org/0000-0001-7592-0299)
- Zhengkun Liu
Institutions
- Tianjin University (CN)
- Singapore General Hospital (SG)
- Ministry of Education (PT)
- Duke-NUS Medical School (SG)
- Tianjin Hospital (CN)
- Ministry of Industry and Information Technology (CN)
Publication Details
- Journal
- BMC Public Health
- Published
- 2025-10-17
- DOI
- https://doi.org/10.1186/s12889-025-24724-7
- Citations
- 1
- Primary Topic
- ECG Monitoring and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 1.39