用症状数据智能调控胃药剂量，减少65%用药量同时保证疗效。
Symptom-Driven Personalized Proton Pump Inhibitors Therapy Using Bayesian Neural Networks and Model Predictive Control
- 基于患者自述症状预测用药需求，结合贝叶斯神经网络与概率控制。
- 虚拟实验显示用药量降低65%，酸抑制成功率超95%。
- 适合长期胃酸治疗患者，无需侵入式监测，可降低用药风险。
质子泵抑制剂（PPIs）是胃酸相关疾病的常规治疗手段，但长期高剂量使用存在显著风险。长期精准控制胃酸面临难以超过72小时的侵入式酸度监测及患者间巨大差异的挑战。本文提出一种非侵入性、以症状为导向的个性化给药框架，仅依据患者报告的反流与消化症状模式调整PPI剂量。基于历史症状评分、饮食及用药数据，贝叶斯神经网络模型预测症状并量化不确定性。这些概率预测输入机会约束型模型预测控制（MPC）算法，动态计算未来用药方案，在最小化药物使用的同时以高置信度维持酸抑制，无需直接测量胃酸。在多种饮食方案与虚拟患者群体的仿真研究中，该学习增强型MPC相比标准固定给药方案，总PPI用量减少65%，且酸抑制概率不低于95%。该方法为个性化PPI治疗提供了实用路径，有效降低治疗负担与过量风险，无需侵入式传感器。
原文摘要 · Abstract (English)
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.
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