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This paper systematically evaluates the impact of classification model selection within the InferBERT framework for causal adverse drug event detection, finding that domain-specific pre-training (BioBERT) outperforms both simpler models and larger LLMs like Med-LLaMA.
This paper presents a hybrid neural-symbolic pipeline for extracting follow-up instructions from clinical notes, using BioBERT and deterministic date arithmetic. It achieves high performance (Pair F1 ~0.99) compared to generative baselines.