Hospitalization Risk

Predicting hospitalization risk is crucial for optimizing healthcare resource allocation and improving patient outcomes. Current research focuses on developing accurate predictive models using diverse data sources, including electronic health records, internet search data, and various surveillance systems, employing machine learning algorithms like deep learning (e.g., LSTM, DNN), gradient boosting, and ensemble methods. These models aim to improve early identification of at-risk individuals, enabling timely interventions and potentially reducing preventable hospitalizations, thereby enhancing healthcare efficiency and patient safety. The development of fair and interpretable models is also a key focus, ensuring equitable access to predictive benefits.