在重症儿童中早期检测血液感染,使用人工智能
Hye-Ji Han1, Kyunghoon Kim1,2, June Dong Park2,3
1Department of Pediatrics, Seoul National University Bundang Hospital, Seongnam, Korea.
Acute and critical care
|November 26, 2024
概括
一个新的机器学习工具可以快速识别危急儿童的血液感染 (BSI). 这种模型有助于早期检测,有可能改善儿科重症监护病房患者的治疗结果.
科学领域:
- 儿科重症监护医药 儿科重症监护医药
- 机器学习在医疗保健中的应用.
- 传染病诊断 传染病诊断 传染病诊断
背景情况:
- 血流感染 (BSI) 在重症患者中存在高死亡风险.
- 在儿科重症监护室 (PICU) 早期发现BSI在诊断上具有挑战性.
- 为儿科BSI开发快速诊断工具至关重要.
研究的目的:
- 开发和验证机器学习 (ML) 模型,以快速识别严重疾病儿童的BSI.
- 提高儿童重症监护机构血液感染早期检测率.
主要方法:
- 利用来自第三级医院的衍生队列 (2020年1月至2023年6月) 进行模型开发.
- 包括年龄,白细胞计数,C反应蛋白,肝酶,葡萄糖和生命体征等变量.
- 比较算法包括额外的树木,随机森林,光梯度增强,极端梯度增强和CatBoost.
主要成果:
- 这项研究分析了263名儿科患者的1806项测量结果.
- 随机森林分类器在接收器操作特征曲线下的面积在开发队列中达到0.874,在验证队列中达到0.762.
- 患有BSI的患者的死亡率显著增加,PICU停留时间更长.
结论:
- 一个机器学习模型成功地开发出来,用于预测严重疾病儿童的BSI,其性能可接受.
- 建议进行进一步的外部验证,以确认该模型在各种临床环境中的有效性.
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