一种自适应记忆神经网络多跳读取与覆盖度机制结合的药物推荐模型

Adaptive Multi-Hop Reading on Memory Neural Network with Selective Coverage Mechanism for Medication Recommendation

药物推荐的目标是依据病人的电子医疗记录生成药物处方,为医生提供临床决策支持.提取电子医疗记录中蕴含的时序模式以及上下文信息,是成功推荐药物的关键.以往研究忽略了病人之间医疗记录数据量存在差异,无法根据不同病人自身情况,调整数据读取过程中的关注重点以及数据读取迭代次数.针对上述问题,本文提出一种选择性覆盖度机制与自适应记忆神经网络读取结合的药物推荐模型.模型使用记忆神经网络存储病人健康状况对应的时序模式编码结果,利用覆盖度机制进行迭代读取过程中的数据过滤与注意力权重调整.同时模型依据病人自身情况,自适应决定记忆神经网络读取次数.基于真实临床数据的实验结果显示,本模型能够自适应地提取电子医疗记录中的重要数据,构建有效的病人健康状况表示向量,进而完成药物推荐.

Medication recommendation aims to make effective prescriptions based on electronic healthcare records(EHRs)of patients,and assists caregivers in clinical decision making.Obtaining temporal patterns of patient conditions as well as contextual information contained in EHRs are the key issues for the success of recommendation.Existing methods do not take the difference in the amount of medical records of different patients into account,and fails to change the focus or number of iterations during information extraction according to personalized patient conditions.To address these problems,the medication recommendation model adaptive multi-hop reading with selective coverage mechanism(AMHSC)is proposed.The model stores encoded temporal patterns with memory neural networks(MemNN),and applies the selective coverage mechanism to balance attention weights over selected information during the attentive multi-hop reading on MemNN.Meanwhile,AMHSC adaptively determines the number of reading hops on MemNN according to personalized patient conditions.Experiments on real-world clinical dataset demonstrate that AMHSC successfully derives important information from EHRs to build informative patient representations for medication recommendation.