Patient Trajectory

Patient trajectory analysis focuses on modeling the temporal evolution of a patient's health status using longitudinal data from electronic health records (EHRs), aiming to improve prediction of clinical outcomes and personalize care. Current research emphasizes the use of deep learning models, including recurrent neural networks, transformers, and neural ordinary differential equations, to handle the complexities of irregular and multimodal EHR data, often incorporating techniques like contrastive learning and adversarial training to mitigate biases. These advancements enable more accurate risk prediction, improved patient stratification for targeted interventions, and the development of interpretable models that provide valuable insights for clinicians and researchers.