Patient Monitoring

Patient monitoring research focuses on developing automated systems for continuous, accurate assessment of patient health, aiming to improve timely interventions and reduce healthcare provider workload. Current efforts leverage diverse data sources (ECG, video, physiological signals, clinical notes) and employ machine learning techniques, including deep learning (CNNs, RNNs, Transformers), self-supervised learning, and reinforcement learning, to analyze these data and predict patient status or risk. These advancements hold significant potential for improving the quality and efficiency of healthcare, particularly in intensive care settings and remote monitoring scenarios, by enabling earlier detection of critical events and personalized interventions.