Detecting cardiovascular diseases using unsupervised machine learning clustering based on electronic medical records
Our findings indicate that the utilization of EMR data can support the development of a robust model for CVD detection through an unsupervised ML approach. Further investigation using longitudinal design is needed to refine the model for its applications in clinical settings.
Authors
- Linli Xie (ORCID: https://orcid.org/0000-0002-9737-9728)
- Hong Jiang (ORCID: https://orcid.org/0000-0002-7260-9646)
- Hai Yan (ORCID: https://orcid.org/0000-0003-0660-7228)
- Lili S. Wei (ORCID: https://orcid.org/0000-0003-2891-5889)
- Yitao Ding (ORCID: https://orcid.org/0000-0002-6552-0838)
- Ming Liu (ORCID: https://orcid.org/0000-0002-6114-445X)
- Chunyu Zhang (ORCID: https://orcid.org/0000-0003-2073-6683)
- Jing Gao (ORCID: https://orcid.org/0000-0001-5099-6991)
- Ying Hu (ORCID: https://orcid.org/0009-0008-4587-3311)
Institutions
- Sun Yat-sen University (CN)
- Fudan University (CN)
- Ministry of Education (KR)
- Shanghai Xuhui Central Hospital (CN)
- Zhongshan Hospital (CN)
- The First Affiliated Hospital, Sun Yat-sen University (CN)
- Obstetrics and Gynecology Hospital of Fudan University (CN)
- Huashan Hospital (CN)
Publication Details
- Journal
- BMC Medical Research Methodology
- Published
- 2024-12-19
- DOI
- https://doi.org/10.1186/s12874-024-02422-z
- Citations
- 13
- Primary Topic
- Artificial Intelligence in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 9.20
Funders
- Shanghai Municipal Health Commission