用机器学习预测血培养感染风险，提升诊疗精准度
A Multi-Phase Analysis of Blood Culture Stewardship: Machine Learning Prediction, Expert Recommendation Assessment, and LLM Automation
- 融合结构化数据与病历文本的机器学习模型
- 模型AUC达0.81，特异性显著优于专家与LLM方案
- 适合临床决策支持系统开发与抗菌药物管理
在135,483例急诊科血培养订单研究中，我们利用电子健康记录（EHR）结构化数据及通过大语言模型（LLM）提取的病历文本，构建机器学习（ML）模型预测菌血症风险。加入文本嵌入后，结构化模型的AUC从0.76提升至0.79，再添加诊断代码后达到0.81。相比人工评审的专家推荐框架和基于LLM的自动化流程，本方法在保持高敏感性的同时显著提高特异性。专家框架敏感性为86%，特异性57%；而LLM虽维持96%高敏感性，但特异性骤降至16%，过度分类阴性样本。结果表明，整合结构化与非结构化数据的机器学习模型可超越现有共识推荐，推动诊断资源优化。
原文摘要 · Abstract (English)
Blood cultures are often over ordered without clear justification, straining healthcare resources and contributing to inappropriate antibiotic use pressures worsened by the global shortage. In study of 135483 emergency department (ED) blood culture orders, we developed machine learning (ML) models to predict the risk of bacteremia using structured electronic health record (EHR) data and provider notes via a large language model (LLM). The structured models AUC improved from 0.76 to 0.79 with note embeddings and reached 0.81 with added diagnosis codes. Compared to an expert recommendation framework applied by human reviewers and an LLM-based pipeline, our ML approach offered higher specificity without compromising sensitivity. The recommendation framework achieved sensitivity 86%, specificity 57%, while the LLM maintained high sensitivity (96%) but over classified negatives, reducing specificity (16%). These findings demonstrate that ML models integrating structured and unstructured data can outperform consensus recommendations, enhancing diagnostic stewardship beyond existing standards of care.
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