基于机器学习的预测 血流感染 在儿科 发烧性中性高症
Jun Sung Park1, Jongkeon Song2, Reenar Yoo3
1Department of Pediatrics, Division of Pediatric Emergency Medicine, Asan Medical Center.
Journal of pediatric hematology/oncology
|December 6, 2024
概括
机器学习模型可以预测血流感染 (BSI) 和性休克 (SS) 在患有发烧性中性不良症 (FN) 的儿科癌症患者中. 这些模型是使用急诊室数据开发的,具有很高的准确性,可以帮助临床决策.
科学领域:
- 儿科瘤学 儿科瘤学
- 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:
- 发烧性中性质衰竭 (FN) 是儿童癌症患者的常见和严重并发症.
- 早期识别血流感染 (BSI) 和败血性休克 (SS) 对于改善结果至关重要.
- 对于FN的紧急部门 (ED) 访问需要快速准确的风险评估.
研究的目的:
- 开发和评估机器学习 (ML) 模型,用于预测儿科癌症患者的BSI和SS在ED出现FN.
- 将ML模型的性能与传统的物流回归模型进行比较.
主要方法:
- 对儿童患者 (<18岁) 进行的回顾性研究,FN患者访问了第三级医院ED (2004年1月至2022年8月).
- 开发基于XGBoost的ML模型,用于BSI和SS预测.
- 分析临床特征,实验室结果和生命体征作为预测因素.
主要成果:
- 分析了4423个FN事件,确定了195个 (4.4%) BSI和107个 (2.4%) SS病例.
- ML模型实现了高预测性能 (BSI的AUC为0.87,SS为0.88),表现优于后勤回归.
- 重要的预测因素包括体温,实验室结果,生命体征和急性髓母细胞白血病诊断.
结论:
- 使用易于获得的ED数据的基于ML的预测模型可以有效地识别患有BSI和SS风险的儿科癌症患者.
- 这些模型为ED医生提供了有价值的决策支持工具,用于管理发烧性中性质不良症.
- 进一步验证和实施这些ML工具可以提高患者的护理和结果.
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