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：临床医生通过审视患者的健康记录并凭借经验开具抗生素处方。然而，若患者存在耐药性，该治疗可能失效。确定耐药性需要耗时耗力的实验室级检测，而由于健康记录由类别型或二元型医疗事件构成，以自动化方式应用临床医生的启发式方法十分困难。本文提出一种新型框架以实现快速临床干预：将健康记录视为图结构，其节点映射自医疗事件，边则表示在给定时间窗口内事件间的对应关系。随后提出一种新型图模型，从这些高维且稀疏的图中提取信息性特征，并实现自动化耐药性分析。该方法将多任务学习整合至公共特征提取图编码器中，从而实现对多种药物的同步分析及学习稳定性提升。基于超过11万名尿路感染患者的大规模数据集，我们验证了所提方法在耐药性预测问题上能够取得优越性能。此外，基于模型的耐药性分析，还可做出与实验室级检测相似的自动化药物推荐。