用机器学习提前预测败血症，降低不必要的血液培养和抗生素使用
TBBC: Predict True Bacteraemia in Blood Cultures via Deep Learning
- 基于随机森林模型，结合急诊数据预测败血症
- 测试集敏感度达0.92，低风险患者识别准确率36.02%
- 适合急诊科用于减少过度检测，降低医疗负担
败血症是一种高发病率和死亡率的血液感染，通过血液培养诊断成本高且耗时。在急诊科开发机器学习模型预测血液培养结果，有望提升诊断效率、降低医疗成本并减少抗生素滥用。本研究基于文献选择CatBoost和随机森林作为最优算法，利用Optuna进行模型优化以提升敏感度。最终的随机森林模型在测试集上达到0.78的ROC AUC，敏感度为0.92。值得注意的是，该模型能准确识别36.02%的低风险患者，且仅有0.85%的假阴性率。在圣安托尼乌斯医院急诊科部署该模型，可显著减少血液培养次数、降低医疗支出并优化抗生素使用。未来研究应聚焦外部验证、探索更先进方法及控制潜在混杂因素，以确保模型泛化能力。
原文摘要 · Abstract (English)
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.
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