Utilizing dynamic treatment information for MACE prediction of acute coronary syndrome

Utilizing dynamic treatment information for MACE prediction of acute coronary syndrome

Abstract Background Main adverse cardiac events (MACE) are essentially composite endpoints for assessing safety and efficacy of treatment processes of acute coronary syndrome (ACS) patients. Timely prediction of MACE is highly valuable for improving the effects of ACS treatments. Most existing tools...

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Bibliographic Details
Main Authors: Huilong Duan, Zhoujian Sun, Wei Dong, Zhengxing Huang
Format: Article
Language:English
Published: BMC 2019-01-01
Series:BMC Medical Informatics and Decision Making
Subjects:
Acute coronary syndrome
MACE prediction
Deep learning
Bidirectional recurrent neural network
Electronic health record
Online Access:http://link.springer.com/article/10.1186/s12911-018-0730-7

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http://link.springer.com/article/10.1186/s12911-018-0730-7

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