标题： 基于症状的个性化质子泵抑制剂治疗：使用贝叶斯神经网络和模型预测控制 显示英文标题

标题： Symptom-Driven Personalized Proton Pump Inhibitors Therapy Using Bayesian Neural Networks and Model Predictive Control

摘要： 质子泵抑制剂（PPIs）是胃酸疾病的标准治疗方法，但在长期高剂量使用时存在显著风险。 精确的长期胃酸控制受到超过72小时的侵入性胃酸监测不切实际以及患者间广泛变异性的挑战。 我们提出了一种非侵入性的、基于症状的框架，仅根据患者报告的胃食管反流和消化症状模式来调整PPI剂量。 一种贝叶斯神经网络预测模型从历史症状评分、饮食和PPI摄入数据中学习预测患者症状并量化其不确定性。 这些概率预测输入到一个机会约束的模型预测控制（MPC）算法中，该算法动态计算未来的PPI剂量，以在无需直接酸度测量的情况下，最大限度地减少药物使用量并以高置信度维持酸度抑制。 在多样化的饮食计划和虚拟患者资料的计算机模拟研究中显示，与标准固定方案相比，我们的学习增强型MPC将总PPI消耗量减少了65%，同时至少以95%的概率保持酸度抑制。 所提出的方法为个性化PPI治疗提供了一条实用路径，在无需侵入性传感器的情况下最小化治疗负担和过量风险。 显示英文摘要

摘要： Proton Pump Inhibitors (PPIs) are the standard of care for gastric acid disorders but carry significant risks when administered chronically at high doses. Precise long-term control of gastric acidity is challenged by the impracticality of invasive gastric acid monitoring beyond 72 hours and wide inter-patient variability. We propose a noninvasive, symptom-based framework that tailors PPI dosing solely on patient-reported reflux and digestive symptom patterns. A Bayesian Neural Network prediction model learns to predict patient symptoms and quantifies its uncertainty from historical symptom scores, meal, and PPIs intake data. These probabilistic forecasts feed a chance-constrained Model Predictive Control (MPC) algorithm that dynamically computes future PPI doses to minimize drug usage while enforcing acid suppression with high confidence - without any direct acid measurement. In silico studies over diverse dietary schedules and virtual patient profiles demonstrate that our learning-augmented MPC reduces total PPI consumption by 65 percent compared to standard fixed regimens, while maintaining acid suppression with at least 95 percent probability. The proposed approach offers a practical path to personalized PPI therapy, minimizing treatment burden and overdose risk without invasive sensors.