Susceptible Temporal Patterns Discovery for Electronic Health Records via Adversarial Attack

The recent advancements in deep neural networks (DNNs) are revolutionizing the healthcare domain. Although many studies try to build medical DNNs model based on historical Electronic Health Records (EHR) and have achieved promising performance in many clinical prediction tasks, recent studies show that DNNs are vulnerable to adversarial attacks. Much of the interest in adversarial examples has stemmed from their ability to shed light on possible limitations of DNNs. However, related research has been receiving sustained attention in computer vision community, how to design adversarial examples for EHR data remains a rarely investigated. To figure out this problem, we propose a novel approach for generating EHR adversarial examples, named as TSAttack, which explores temporal structure contained in EHR to achieve an effective and efficient attack. Based on the generated EHR adversarial examples, we further propose a procedure to discover susceptible temporal patterns (STP) in a patient’s medical records, which provide clinical decision support for dynamic monitoring. Extensive experiments on the real-world longitudinal EHR database MIMIC-III have demonstrated the effectiveness of our approach is yielding better performance in adversarial settings.