Deep learning based patient representation learning framework of heterogeneous temporal events data

Luchen LIU; Jianhao SHEN; Ming ZHANG; Zichang WANG; Haoran LI; Zequn LIU

doi:10.11959/j.issn.2096-0271.2019003

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Deep learning based patient representation learning framework of heterogeneous temporal events data

Topic:Big Data on Health Care | 更新时间：2024-06-03

- Deep learning based patient representation learning framework of heterogeneous temporal events data
- Big data research Vol. 5, Issue 1, Pages: 2019003(2019)
- 作者机构：
- 作者简介：
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- DOI：10.11959/j.issn.2096-0271.2019003
  CLC： TP183
- Online First：2019-01，
  
  Published：15 January 2019
- 稿件说明：
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Luchen LIU, Jianhao SHEN, Ming ZHANG, et al. Deep learning based patient representation learning framework of heterogeneous temporal events data[J]. Big data research, 2019, 5(1): 2019003.
DOI：

Luchen LIU, Jianhao SHEN, Ming ZHANG, et al. Deep learning based patient representation learning framework of heterogeneous temporal events data[J]. Big data research, 2019, 5(1): 2019003. DOI： 10.11959/j.issn.2096-0271.2019003.

摘要

患者数据的表示学习可以将患者历史信息综合表达为一个向量，用于预测未来可能发生的疾病。患者的历史记录可以被建模为多来源数据构成的采样频率差异很大、包含非线性时序关系的异构时序事件。提出了一个新的异构事件长短期记忆表示学习框架，用于学习患者异构时序事件的联合表征。异构事件长短期记忆模型加入了一个可以控制事件访问频率的门，以对不同事件的不规则采样频率建模，同时抓住事件中的复杂时序依赖关系。真实临床数据的实验表明，该方法可以在一系列先进模型的基础上，提升死亡预测和异常实验结果预测的准确度。

Abstract

Patient representation embeds patients' longitude records from multiple sources into continuous low-dimension vectors

which can be used to predict whether a disease will happen in the future. However

the problem is very challenging since patients' history records contain multiple heterogeneous temporal events. The visiting patterns of different types of events vary significantly

and there exist complex nonlinear relationships between different events. A novel model for learning the joint representation of heterogeneous temporal events was proposed. The model adds a new gate to control the visiting rates of different events which effectively models the irregular patterns of different events and their nonlinear correlations. Experiment results with real-world clinical data on the tasks of predicting death and abnormal lab tests prove the effectiveness of the proposed approach over competitive baselines.

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