FERMAT: An Autoregressive Clinical Foundation Model for Disease Risk and Clinical Event Prediction

Clinical foundation models learn patterns in longitudinal health records that can be reused across prediction tasks. However, the relative strengths of representation-based and generation-based prediction across clinical tasks remain unclear. Using longitudinal electronic health records from 3.77 million patients at a single hospital, we developed FERMAT (Foundation model for Exploring Real-world Multimodal health data using Autoregressive Trajectory modeling), a 65-million-parameter clinical foundation model. We compared predictions from task-specific predictors trained on patient representat

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