Deep Learning Technologies for Time Series Anomaly Detection in Healthcare: A Review

Medical time series data often exhibit intricate and dynamic patterns. With the rapid advancement of medical digitization, deep learning-based time series anomaly detection techniques have found extensive applications in the healthcare field, such as detecting irregular heart rhythms and monitoring patients’ vital signs. To fully leverage digitized medical records to identify anomalies in healthcare and address key challenges in precise anomaly detection, this paper provides a comprehensive review of deep learning-based anomaly detection techniques applied to medical time series data. By reviewing and summarizing the relevant research, this paper explores the deep learning-based time series anomaly detection techniques within the medical and health domain, analyzing the strengths and limitations of different deep learning architectures and algorithms in tackling specific medical tasks. Lastly, we discuss the challenges faced by this field and outline future research directions. By reviewing and summarizing advanced deep learning methods for time series anomaly detection in medical applications in recent years, this study contributes to the advancement of healthcare analytics, aiming to enhance patient treatment outcomes.