Multi-disease prediction using LSTM recurrent neural networks
Multi-disease prediction using LSTM recurrent neural networks
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DOI：
10.1016/j.eswa.2021.114905
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发表时间：
2021-04-06
影响因子：
8.5
通讯作者：
Liu, Yuan
中科院分区：
文献类型：
--
作者：
Men, Lu;Ilk, Noyan;Liu, Yuan
未来临床事件的预测（例如，疾病诊断）是医疗保健信息学研究中的重要机器学习任务。在这项工作中，我们提出了一种深度学习方法来执行多疾病预测，以实现智能临床决策支持。所提出的方法利用长短期记忆网络并利用两种机制对其进行扩展（即，时间感知和注意力为基础），根据患者的临床访视记录进行多标签分类。前一种机制（时间感知）用于处理临床访视中的时间不规则性，而后一种机制（基于注意力）有助于确定每次访视对预测任务的重要性。使用从中国东南部一家医院收集的大型临床记录数据集（超过500万条记录），我们表明，我们提出的方法在预测未来疾病诊断方面优于各种传统和深度学习方法。我们进一步研究了不同的时间间隔选择的时间感知机制的影响，并比较现有的基于注意力的机制与我们的研究中提出的一个性能。我们的工作对通过使用智能系统支持医生诊断以及更广泛地提高医疗服务质量具有重要意义。
Prediction of future clinical events (e.g., disease diagnoses) is an important machine learning task in healthcare informatics research. In this work, we propose a deep learning approach to perform multi-disease prediction for intelligent clinical decision support. The proposed approach utilizes a long short-term memory network and extends it with two mechanisms (i.e., time-aware and attention-based) to conduct multi-label classification based on patients' clinical visit records. The former mechanism (time-aware) is used to handle the temporal irregularity across clinical visits whereas the latter mechanism (attention-based) assists in determining the importance of each visit for the prediction task. Using a large clinical record data set (over 5 million records) collected from a hospital in Southeast China, we show that our proposed approach outperforms a variety of traditional and deep learning methods in predicting future disease diagnoses. We further study the impacts of different time interval choices for the time-aware mechanism and compare the performances of existing attention-based mechanisms with the one proposed in our study. Our work has implications for supporting physician diagnoses via the use of intelligent systems and more broadly for improving the quality of healthcare service.