Hospital Readmission Prediction

Predicting hospital readmissions aims to improve healthcare efficiency and patient outcomes by identifying individuals at high risk of readmission. Current research heavily utilizes machine learning, focusing on multimodal approaches that integrate electronic health records, medical images, and clinical notes, often employing deep learning architectures like transformers and graph neural networks to capture complex temporal and spatial relationships within patient data. These models strive for improved predictive accuracy while also enhancing interpretability, a crucial aspect for clinical adoption and trust, leading to better resource allocation and potentially reduced healthcare costs.