Clinicians prescribe antibiotics by looking at the patient's health record with an experienced eye. However, the therapy might be rendered futile if the patient has drug resistance. Determining drug resistance requires time-consuming laboratory-level testing while applying clinicians' heuristics in an automated way is difficult due to the categorical or binary medical events that constitute health records. In this paper, we propose a novel framework for rapid clinical intervention by viewing health records as graphs whose nodes are mapped from medical events and edges as correspondence between events in given a time window. A novel graph-based model is then proposed to extract informative features and yield automated drug resistance analysis from those high-dimensional and scarce graphs. The proposed method integrates multi-task learning into a common feature extracting graph encoder for simultaneous analyses of multiple drugs as well as stabilizing learning. On a massive dataset comprising over 110,000 patients with urinary tract infections, we verify the proposed method is capable of attaining superior performance on the drug resistance prediction problem. Furthermore, automated drug recommendations resemblant to laboratory-level testing can also be made based on the model resistance analysis.

翻译：临床医生凭借经验审视患者的健康记录来开具抗生素处方。然而，若患者存在药物耐药性，该治疗可能无效。确定药物耐药性需要耗时耗力的实验室级检测，而由于健康记录由分类或二值化的医疗事件构成，以自动化方式应用临床医生的启发式方法颇具挑战。本文提出一种面向快速临床干预的新框架：将健康记录视为图结构，其中节点映射自医疗事件，边表示在给定时间窗口内事件之间的对应关系。随后，我们提出一种新型图基模型，从这些高维且稀疏的图中提取信息特征并生成自动化的药物耐药性分析。该方法将多任务学习集成至共享特征提取的图编码器中，以实现对多种药物的同步分析并稳定学习过程。基于包含超过110,000名尿路感染患者的大规模数据集，我们验证了该方法在药物耐药性预测问题上能够取得优越性能。此外，依据模型耐药性分析结果，还可生成与实验室级检测相似的自动化药物推荐建议。