Enhancing Patient Selection in Sepsis Clinical Trials Design Through an AI Enrichment Strategy

Developed an AI model to predict long-course sepsis patients for clinical trial enrollment, enhancing patient homogeneity. Utilized conformal prediction to assess model output uncertainty, aiding informed decision-making. Model performance validated internally and externally, with a web-based calculator aiding clinician understanding and feedback, potentially improving clinical decision-making.