用生理数据预测提前判断换药时机，提升抗生素管理效率。
Optimising antibiotic switching via forecasting of patient physiology
- 用神经过程建模生命体征轨迹，按指南预测换药时机
- 在美英两组数据中，选出相关患者数达随机选择的2.2-3.2倍
- 不依赖历史决策，可动态适配新指南，适合临床医生参考
及时从静脉注射转为口服抗生素可缩短住院时间、减少导管感染并降低医疗成本，但英国五分之一符合换药标准的患者仍持续使用静脉抗生素。现有临床辅助系统多基于历史决策学习，延续了实际实践中的延迟与不一致。本文提出利用神经过程概率化建模生命体征轨迹，通过将预测结果与临床指南对比来判断换药可行性，而非模仿过往行为，并对患者进行排序以优先安排临床评估。该方法输出可解释，无需重训练即可适应更新的指南，同时保留临床判断空间。在MIMIC-IV（美国重症监护，6,333例）和UCLH（英国大型城市学术医院集团，10,584例）上验证，系统选出的相关患者数量是随机选择的2.2至3.2倍。结果表明，预测患者生理变化为抗生素管理决策支持提供了可靠基础。
原文摘要 · Abstract (English)
Timely transition from intravenous (IV) to oral antibiotic therapy shortens hospital stays, reduces catheter-related infections, and lowers healthcare costs, yet one in five patients in England remain on IV antibiotics despite meeting switching criteria. Clinical decision support systems can improve switching rates, but approaches that learn from historical decisions reproduce the delays and inconsistencies of routine practice. We propose using neural processes to model vital sign trajectories probabilistically, predicting switch-readiness by comparing forecasts against clinical guidelines rather than learning from past actions, and ranking patients to prioritise clinical review. The design yields interpretable outputs, adapts to updated guidelines without retraining, and preserves clinical judgement. Validated on MIMIC-IV (US intensive care, 6,333 encounters) and UCLH (a large urban academic UK hospital group, 10,584 encounters), the system selects 2.2-3.2$\times$ more relevant patients than random. Our results demonstrate that forecasting patient physiology offers a principled foundation for decision support in antibiotic stewardship.
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