Clinical Prediction Model

Clinical prediction models use patient data to forecast health outcomes, aiming to improve preventative care and treatment decisions. Current research emphasizes developing robust models using various machine learning techniques, including large language models (LLMs) for structured data and BERT-based models for unstructured clinical notes, while also addressing challenges like data heterogeneity across institutions and ensuring model explainability and fairness. This field is crucial for advancing personalized medicine, improving healthcare efficiency, and fostering trust in AI-driven clinical tools through rigorous validation and transparent methodologies.