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数据用于诊断:机器学习辅助预测血液感染

Mallika Sengupta¹, Sunitaj Parvin¹, Aditya Kundu¹

¹Department of Microbiology, All India Institute of Medical Sciences Kalyani, West Bengal, India.

|December 15, 2025

概括

机器学习模型使用常规血液检查准确预测血流感染 (BSI). 这种方法有助于早期诊断和指导抗生素治疗,特别是在资源有限的环境中.

科学领域:

临床微生物学临床微生物学
医疗信息学医学信息学
机器学习在医疗保健中的应用.

背景情况:

血流感染 (BSI) 的早期预测对于患者的治疗结果至关重要.
描述微生物和抗微生物耐药性 (AMR) 概况有助于向治疗.
常规血液学参数为感染提供了潜在的预测价值.

研究的目的:

用血液学数据评估机器学习 (ML) 算法,用于早期BSI预测.
描述血液感染的微生物和AMR概况.
评估ML模型在指导实证抗生素治疗中的有用性.

主要方法:

在 AIIMS Kalyani 的回顾性观察研究 (2023 年 1 月至 2024 年 12 月).
收集血液培养结果,抗微生物敏感性测试 (AST) 概况和16个常规血液学参数.
使用Python开发和评估ML模型 (逻辑回归,决策树,随机森林,SVM).

主要成果:

随机森林模型显示出最高的预测能力 (准确率:87%,AUC:0.70).
BSI的关键预测因素包括中性粒细胞计数,C反应蛋白 (CRP) 和全白细胞计数 (TLC).
主要的分离物是高耐药性的大肠杆菌,肺炎菌,金黄色葡萄球菌和空气真菌.

结论:

机器学习模型显示了使用可访问的血液学数据进行早期BSI预测的前景.
将ML与AMR监测相结合,可以加速诊断,并为实证抗生素策略提供信息.
这种方法在改善低资源环境中的BSI管理方面尤为有价值.

关键词:

血流感染血流感染抗微生物耐药性抗微生物耐药性机器学习是机器学习.预测模型预测模型

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