Dynamic perioperative risk prediction requires models to anticipate evolving physiological deterioration under strict prediction-time information boundaries. We introduce PERIBENCH, a protocol-aware benchmark built from high-frequency intraoperative signals. PERIBENCH defines 18 clinically motivated tasks spanning future risk classification, persistent deterioration prediction, continuous vital-sign regression, and case-level prognosis, and evaluates 37 systems across numerical predictors, deep sequential models, time-series foundation models, and LLM-oriented language interfaces. Rather than using a single undifferentiated leaderboard, PERIBENCH separates supervised or adapted prediction, direct pretrained transfer, and prompt-based language-interface inference into protocol-compatible pools. Experiments show that model sophistication does not translate monotonically into better perioperative prediction: tree-based references remain strong on many classification endpoints, deep sequential models are competitive on selected physiological tasks, direct foundation-model transfer is heterogeneous, and language interfaces capture useful classification signals but remain less reliable for regression, calibration, alert burden, and abnormal-region prediction. PERIBENCH provides a structured benchmark for studying dynamic perioperative prediction from numerical models to language interfaces under shared task semantics and explicit protocol boundaries. Code is available at https://github.com/Kdyyh/PERIBENCH.

Sequence labeling of biomedical entities, e.g., side effects or phenotypes, was a long-term task in BioNLP and MedNLP communities. Thanks to effects made among these communities, adverse reaction NER has developed dramatically in recent years. As an illuminative application, to achieve knowledge discovery via the combination of the text mining result and bioinformatics idea shed lights on the pharmacological mechanism research.