Scaling experiments on structured medical claims data show task-dependent saturation: disease incidence prediction benefits from models up to 101M parameters while medication prediction saturates at 11M, with all models outperforming a LightGBM baseline.
TransformEHR: Transformer-based encoder-decoder generative model to enhance pre- diction of disease outcomes using electronic health records,
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A Nationwide Japanese Medical Claims Foundation Model: Balancing Model Scaling and Task-Specific Computational Efficiency
Scaling experiments on structured medical claims data show task-dependent saturation: disease incidence prediction benefits from models up to 101M parameters while medication prediction saturates at 11M, with all models outperforming a LightGBM baseline.