Fig. 2

Diagram illustrating the development of ML models for initializing the TIA screening ML-LHS unit. This unit is designed to enhance inclusive risk-based TIA screening in both urban and rural populations. Step 1: Building an inclusive model from hospital EMR data using a data-centric approach. Step 2: Refining this model into a more practical model by applying a quantitative distribution of TIA risk factors. Step 3: Initializing the ML-LHS unit by internally validating the practical model using new EMR data. Step 4: Externally validating the practical model with data from different hospitals. Post-initialization, the unit is prepared to provide risk prediction for patients in the clinical research network (CRN). After deployment for routine clinical screening, at-risk patients will be identified for early detection. Continuous learning cycles, fed by new data from the service, will facilitate ongoing building and validation of models within the embedded research. Pragmatic clinical trials (PCT) will generate robust evidence for the rapid evolution of the ML-LHS unit. TIA: transient ischemic attack. ML: machine learning. LHS: learning health system