MedGate-Fusion integrates clinical narratives and biomarkers for stroke risk
Researchers have proposed MedGate-Fusion, a multi-modal gated architecture designed to improve prospective stroke risk stratification in primary care settings. The system addresses the challenge of early risk signals being scattered across routine biomarkers and unstructured clinical notes by integrating transformer-based embeddings of first-encounter narratives with ten routinely recorded risk…
Key points
- MedGate-Fusion integrates transformer-based narrative embeddings with ten routine risk markers.
- Study used 102,736 unique patient records from the Canadian Primary Care Sentinel Surveillance Network.
- Dictionary-based redaction was applied to remove stroke-related terms to prevent target leakage.
The study utilized electronic medical record data from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN). From an initial pool of 808,921 encounter-level observations, the team constructed a first-encounter cohort, retaining 102,736 unique patient records that had non-empty narratives and sufficient data to evaluate a five-year stroke outcome. To ensure data integrity and reduce explicit target leakage from diagnostic mentions in notes, the researchers applied dictionary-based redaction of stroke-related terms prior to semantic encoding.
This approach aims to provide a more comprehensive view of patient risk by combining structured physiological data with unstructured semantic information. By focusing on first-encounter data, the model seeks to identify risk factors early in the patient journey, potentially allowing for earlier intervention in primary care environments.
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