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.

翻译：及时从静脉注射（IV）转为口服抗生素治疗可缩短住院时间、减少导管相关感染并降低医疗成本，然而在英格兰，尽管符合转换标准，仍有五分之一的患者继续接受静脉抗生素治疗。临床决策支持系统能够提高转换率，但基于历史决策学习的方法会重现常规实践中存在的延迟与不一致性。我们提出使用神经过程对生命体征轨迹进行概率建模，通过将预测结果与临床指南进行比较（而非从既往操作中学习）来预测转换准备度，并对患者进行排序以优先安排临床评估。该设计产生可解释的输出，能够适应更新的指南而无需重新训练，并保留临床判断力。在MIMIC-IV（美国重症监护数据集，6,333例诊疗记录）和UCLH（英国大型城市学术医院集团，10,584例诊疗记录）验证中，该系统选择的关联患者数量是随机选择的2.2-3.2倍。我们的研究结果表明，患者生理状态预测为抗生素管理决策支持提供了理论依据。