Clinical Time Series

Clinical time series analysis focuses on extracting meaningful insights from sequential patient data, such as vital signs and lab results, to improve diagnosis, prognosis, and treatment. Current research emphasizes developing robust deep learning models, including transformers, recurrent neural networks (like GRUs and LSTMs), and contrastive learning methods, to handle the inherent irregularities, high dimensionality, and multi-modality of these datasets. This work leverages both single and multi-modal data, often incorporating techniques like self-supervised learning and advanced feature embedding strategies to enhance model performance and interpretability, ultimately aiming to improve patient care and advance precision medicine.