In this work we present OptAB, the first completely data-driven online-updateable antibiotic selection model based on Artificial Intelligence for Sepsis patients accounting for side-effects. OptAB performs an iterative optimal antibiotic selection for real-world Sepsis patients focussing on minimizing the Sepsis-related organ failure score (SOFA-Score) as treatment success while accounting for nephrotoxicity and hepatotoxicity as serious antibiotic side-effects. OptAB provides disease progression forecasts for (combinations of) the antibiotics Vancomycin, Ceftriaxone and Piperacillin/Tazobactam and learns realistic treatment influences on the SOFA-Score and the laboratory values creatinine, bilirubin total and alanine-transaminase indicating possible side-effects. OptAB is based on a hybrid neural network differential equation algorithm and can handle the special characteristics of patient data including irregular measurements, a large amount of missing values and time-dependent confounding. OptAB’s selected optimal antibiotics exhibit faster efficacy than the administered antibiotics.

在这项工作中，我们提出了 OptAB，这是第一个完全数据驱动的基于人工智能的在线可更新抗生素选择模型，适用于考虑副作用的脓毒症患者。OptAB 对真实世界的脓毒症患者进行迭代最佳抗生素选择，重点是最小化脓毒症相关器官衰竭评分 （SOFA-Score） 作为治疗成功，同时将肾毒性和肝毒性作为严重的抗生素副作用。OptAB 提供抗生素万古霉素、头孢曲松和哌拉西林/他唑巴坦（组合）的疾病进展预测，并了解治疗对 SOFA 评分和表明可能副作用的实验室值肌酐、胆红素总量和丙氨酸转氨酶的实际治疗影响。OptAB 基于混合神经网络微分方程算法，可以处理患者数据的特殊特性，包括不规则测量、大量缺失值和瞬态混杂。OptAB 选择的最佳抗生素比服用的抗生素表现出更快的疗效。