Electronic medical records and machine learning for sepsis prediction

This project explores the use of Large language and Machine learning models for Sepsis prediction in medical applications. The aim is to develop tool for the prediction and early detection of sepsis in different clinical contexts. To estimate the impact on patient outcomes and costs of the EMR model-based decision support tool simulating its integration in the routine clinical workflow, and, through the simulation and decision curves, inform model selection, refinement, and implementation. And to build the model in the EMR system as real-time decision support tool.