论文标题
总理：电子记录中医疗处方的个性化建议
PREMIER: Personalized REcommendation for Medical prescrIptions from Electronic Records
论文作者
论文摘要
电子健康记录（EHR）的广泛采用导致在患者的病史，诊断，处方和实验室测试上积累了大量数据。推荐技术的进步有可能利用这些信息来帮助医生个性化规定的药物。在这项工作中，我们设计了一个两阶段的基于注意力的个性化药物推荐系统，称为Premier，该系统结合了EHR的信息，以建议一组药物。我们的系统考虑了药物之间的相互作用，以最大程度地减少患者的不良影响。我们利用系统中的各种注意力重量来计算推荐药物的信息来源的贡献。实验结果对模仿-III和专有的门诊数据集表明，高级人士的表现优于最先进的药物建议系统，同时实现了准确性和药物 - 药物相互作用之间的最佳折衷。还提出了两项案例研究，表明总理提供的理由是适当的，并且与临床实践保持一致。
The broad adoption of Electronic Health Records (EHR) has led to vast amounts of data being accumulated on a patient's history, diagnosis, prescriptions, and lab tests. Advances in recommender technologies have the potential to utilize this information to help doctors personalize the prescribed medications. In this work, we design a two-stage attention-based personalized medication recommender system called PREMIER which incorporates information from the EHR to suggest a set of medications. Our system takes into account the interactions among drugs in order to minimize the adverse effects for the patient. We utilize the various attention weights in the system to compute the contributions from the information sources for the recommended medications. Experiment results on MIMIC-III and a proprietary outpatient dataset show that PREMIER outperforms state-of-the-art medication recommendation systems while achieving the best tradeoff between accuracy and drug-drug interaction. Two case studies are also presented demonstrating that the justifications provided by PREMIER are appropriate and aligned to clinical practices.
