Bacteraemia, a bloodstream infection with high morbidity and mortality rates, poses significant diagnostic challenges. Accurate diagnosis through blood cultures is resource-intensive. Developing a machine learning model to predict blood culture outcomes in emergency departments offers potential for improved diagnosis, reduced healthcare costs, and mitigated antibiotic use.This thesis aims to identify optimal machine learning techniques for predicting bacteraemia and develop a predictive model using data from St. Antonius Hospital's emergency department. Based on current literature, CatBoost and Random Forest were selected as the most promising techniques. Model optimization using Optuna prioritized sensitivity.The final Random Forest model achieved an ROC AUC of 0.78 and demonstrated 0.92 sensitivity on the test set. Notably, it accurately identified 36.02% of patients at low risk of bacteraemia, with only 0.85% false negatives.Implementation of this model in St. Antonius Hospital's emergency department could reduce blood cultures, healthcare costs, and antibiotic treatments. Future studies should focus on external validation, exploring advanced techniques, and addressing potential confounders to ensure model generalizability.

A team of researchers at the Medical University of Vienna has recently evaluated the effectiveness of machine learning strategies to identify bacteraemia in patients affected by systemic inflammatory response syndrome (SIRS). Their study, published in Scientific Reports, gathered discouraging results, as machine learning methods could not achieve better accuracy than current diagnostic techniques. Bacteraemia is a frequent medical condition characterized by the presence of bacteria in the blood, with a mortality rate ranging between 13 percent and 21 percent. Past research suggests that a number of factors are associated with the risk of developing this condition, including advanced age, urinary or indwelling vascular catheter, chemotherapy, and immunosuppressive therapies. Diagnosing bacteraemia early is of crucial importance for the survival of affected patients, as they require prompt treatment with appropriate antibiotics.