基于深度学习的异构时序事件患者数据表示学习框架

doi:10.11959/j.issn.2096-0271.2019003

Abstract

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.

Key words: electronic health record, patient representation learning, heterogeneous temporal events, deep learning

CLC Number:

TP183

Luchen LIU, Jianhao SHEN, Ming ZHANG, Zichang WANG, Haoran LI, Zequn LIU. Deep learning based patient representation learning framework of heterogeneous temporal events data[J]. Big Data Research, 2019, 5(1): 25-38.

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事件来源	举例	事件类型数量
化验指标	血细胞压积、白细胞数	525
生理指标	心率、呼吸率	385
药物注射	0.9%生理盐水、5%葡糖糖溶液	60
临床症状	异位类型、运动反应	2382
治疗程序	心电图、有创呼吸机	36

方法	死亡预测			钾离子异常预测
方法	AUC	AP	AUC	AP
独立LSTM	0.877 1	0.557 3	0.719 6	0.296 9
共享参数的独立LSTM	0.806 4	0.530 1	0.530 8	0.109 8
phased LSTM	0.847 4	0.490 0	0.772 2	0.357 5
clock-work RNN	0.840 0	0.718 1	0.651 6	0.220 8
Retain	0.896 7	0.580 8	0.732 5	0.309 6
LSTM + 事件嵌入	0.946 6	0.744 5	0.723 1	0.302 1
HE-LSTM	0.951 6	0.768 7	0.798 7	0.391 4

	事件门的不同设置	只用事件过滤器的模型	只用相位门的模型	HELSTM
死亡预测	AUC（第一轮）	0.910 5	0.930 1	0.937 0
	AUC	0.951 8	0.947 1	0.951 6
	AP（第一轮）	0.604 8	0.685 6	0.709 4
	AP	0.767 9	0.746 7	0.768 7
	交叉熵（第一轮）	0.183 5	0.156 1	0.147 9
	交叉熵	0.130 1	0.136 9	0.129 7
钾离子异常预测	AUC（第一轮）	0.674 7	0.705 0	0.727 5
	AUC	0.755 9	0.794 5	0.798 7
	AP（第一轮）	0.240 3	0.275 2	0.296 5
	AP	0.341 0	0.387 5	0.391
	交叉熵（第一轮）	0.344 8	0.337 3	0.329 8
	交叉熵	0.317 8	0.301 9	0.300 3

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

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