TBBC: Predict True Bacteraemia in Blood Cultures via Deep Learning

TL;DR

This study develops a deep learning-based predictive model for bacteraemia using hospital data, achieving high sensitivity and potential to reduce unnecessary blood cultures and antibiotic use.

Contribution

It identifies optimal machine learning techniques and creates a model with high sensitivity for predicting bacteraemia in emergency settings.

Findings

Random Forest model achieved ROC AUC of 0.78

Model demonstrated 0.92 sensitivity on test data

Identified 36.02% of low-risk patients with minimal false negatives

Abstract

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.