论文标题
使用索赔代码通过分层时间感知神经网络进行患者ADE风险预测
Patient ADE Risk Prediction through Hierarchical Time-Aware Neural Network Using Claim Codes
论文作者
论文摘要
不良药物事件（ADE）是一个严重的健康问题，可能会威胁生命。尽管已经在药物和AE之间进行了许多检测相关性进行了许多研究，但对个性化ADE风险预测进行了有限的研究。在治疗替代方案中，避免造成严重AE的可能性很可能的药物可以帮助医生为患者提供更安全的治疗。关于个性化ADE风险预测的现有工作使用当前医疗访问中获得的信息。但是，另一方面，病史揭示了每个患者的独特特征和全面的医疗信息。这项研究的目的是根据索赔代码中记录的患者病史，评估目标药物可能诱发目标患者的个性化ADE风险，这些病史提供了有关诊断，吸毒，相关的医疗用品的信息，除了账单信息外。我们开发了HTNNR模型（用于ADE风险的分层时间感知神经网络），该模型捕获了索赔代码及其关系的特征。经验评估表明，拟议的HTNNR模型基本上优于比较方法，尤其是对于稀有药物而言。
Adverse drug events (ADEs) are a serious health problem that can be life-threatening. While a lot of studies have been performed on detect correlation between a drug and an AE, limited studies have been conducted on personalized ADE risk prediction. Among treatment alternatives, avoiding the drug that has high likelihood of causing severe AE can help physicians to provide safer treatment to patients. Existing work on personalized ADE risk prediction uses the information obtained in the current medical visit. However, on the other hand, medical history reveals each patient's unique characteristics and comprehensive medical information. The goal of this study is to assess personalized ADE risks that a target drug may induce on a target patient, based on patient medical history recorded in claims codes, which provide information about diagnosis, drugs taken, related medical supplies besides billing information. We developed a HTNNR model (Hierarchical Time-aware Neural Network for ADE Risk) that capture characteristics of claim codes and their relationship. The empirical evaluation show that the proposed HTNNR model substantially outperforms the comparison methods, especially for rare drugs.
