Clinical Prediction

Clinical prediction studies use patient data to forecast events such as sepsis, readmission, disease progression, treatment response, and changes in severity. Common approaches range from logistic regression and gradient-boosted trees to temporal neural networks, multimodal models, and language models that work with electronic health records; evaluations often use datasets such as MIMIC, eICU, and EHRSHOT. Researchers also develop methods for interpretable predictions, uncertainty estimation, privacy-preserving collaboration, and adapting models to new hospitals or populations. A central challenge is that strong results on internal benchmarks may not transfer across sites or patient groups, so external validation, calibration, leakage control, and evidence of clinical usefulness remain important.