Spatio-Temporal Meta-Learning for Patient Trajectory in Longitudinal Electronic Health Records: A Review

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Research on spatiotemporal hyper-learning applied to longitudinal electronic health records (EHRs) provides a concise yet helpful summary. The research problems, innovative methodology, datasets (with a focus on MIMIC-IV), expected findings, and implications of spatiotemporal hyper-learning for patient pathway modelling are all covered in detail in this study. With more and more electronic health record data being available, predictive analytics have grown, particularly in the areas of patient pathways and clinical outcomes. Different problems arise due to the complexity caused by changes in time and the accompanying clinical characteristics. We require state-of-the-art modelling approaches that incorporate spatiotemporal dimensions. By monitoring the development of clinical features and intricate spatial connections, spatiotemporal hyper-learning frameworks have the potential to enhance patient route modelling. The approach is novel because it can adapt to new patient groups and clinical conditions through hyper-learning. Though it is briefly mentioned in the abstract, the MIMIC-IV dataset confirms the framework; however, the method section explains the approach, strategies, and algorithms. Most courses address data processing, feature extraction, learning to represent spatial and temporal components, and hyper-learning for fast adaptation to new patient data. Stochastic hyper-learning has the potential to outperform more conventional methods of prediction in terms of accuracy and flexibility. This method has the potential to enhance clinical decision-making since it can detect patient trajectories.