Development of transient ischemic attack risk prediction model suitable for initializing a learning health system unit using electronic medical records
This study developed the first inclusive and practical TIA XGBoost model from full hospital EHR and initiated the first TIA risk prediction ML-LHS unit. This TIA model, which requires only 20 variables, enables the ML-LHS to serve not only patients in hospitals but also those in resource-limited clinics. These results have significant implications for expanding risk-based TIA screening in community and rural clinics, thereby enhancing early detection of TIA among underserved populations and improving health equity. The novel protocol used in this study is also applicable for initiating ML-LHS units for various preventable diseases, providing a new system-level approach to responsible AI development and applications.
Authors
- S. Ye (ORCID: https://orcid.org/0000-0003-1457-5019)
- Ruobing Han (ORCID: https://orcid.org/0000-0001-5932-9829)
- Ran Huang (ORCID: https://orcid.org/0000-0002-3412-5042)
- Qinghua Li (ORCID: https://orcid.org/0000-0002-4547-8513)
- Jian Wen (ORCID: https://orcid.org/0000-0002-6370-3964)
- Bairong Shen (ORCID: https://orcid.org/0000-0003-2899-1531)
- Cheng Jung Li (ORCID: https://orcid.org/0000-0001-6110-8099)
- Anjun Chen (ORCID: https://orcid.org/0000-0003-4209-8301)
- Tianmei Zhang (ORCID: https://orcid.org/0000-0002-5241-6038)
Institutions
- Guilin Medical University (CN)
- Sichuan University (CN)
- West China Hospital of Sichuan University (CN)
- Palo Alto Institute (US)
Publication Details
- Journal
- BMC Medical Informatics and Decision Making
- Published
- 2024-12-18
- DOI
- https://doi.org/10.1186/s12911-024-02767-x
- Citations
- 3
- Primary Topic
- Artificial Intelligence in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 2.12
Funders
- Guilin Science and Technology Bureau
- Sichuan Province Science and Technology Support Program