Medical Time Series

Medical time series analysis focuses on extracting meaningful information from sequential physiological data to improve healthcare. Current research emphasizes developing robust and generalizable models, including transformers, large language models, and generative models like variational autoencoders, to address challenges like data heterogeneity, missing values, and imbalanced datasets. These advancements aim to improve diagnostic accuracy, personalize treatment, and enhance the efficiency of clinical decision-making across various medical domains. A key trend is the use of self-supervised and few-shot learning techniques to overcome limitations posed by scarce labeled data.