Health Risk Prediction

Health risk prediction uses patient data to forecast future health outcomes, aiming to improve preventative care and personalize treatment. Current research focuses on leveraging electronic health records (EHRs) and wearable sensor data, employing deep learning models like convolutional and recurrent neural networks, diffusion models, and large language models to improve prediction accuracy and address challenges like data sparsity and missing values. These advancements are crucial for enhancing healthcare decision-making, enabling earlier interventions, and potentially reducing healthcare costs, although challenges remain in addressing algorithmic bias and ensuring model robustness against adversarial attacks.