{"ID":2895257,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.09685","arxiv_id":"2507.09685","title":"Symptom-Driven Personalized Proton Pump Inhibitors Therapy Using Bayesian Neural Networks and Model Predictive Control","abstract":"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.","short_abstract":"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 variabili...","url_abs":"https://arxiv.org/abs/2507.09685","url_pdf":"https://arxiv.org/pdf/2507.09685v1","authors":"[\"Yutong Li\",\"Ilya Kolmanovsky\"]","published":"2025-07-13T15:46:41Z","proceeding":"eess.SY","tasks":"[\"eess.SY\",\"cs.LG\"]","methods":"[]","has_code":false}