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使用机器学习预测儿童肺炎护理的升级:回顾性分析和模型开发
Oguzhan Serin1, Izzet Turkalp Akbasli1, Sena Bocutcu Cetin1
1Department of Pediatrics, Hacettepe University Medical School, Gevher Nesibe Avenue, Altindag, Ankara, 06230, Turkey, 90 3051350.
机器学习准确地预测了儿童肺炎病例需要升级护理的需求. 这种工具有助于医生治疗儿童肺炎,改善患者的治疗结果.
科学领域:
- 儿科医学 儿科医学 儿科医学
- 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:
- 肺炎是5岁以下儿童死亡的主要原因之一.
- 现有的机器学习 (ML) 应用在肺炎诊断中没有专注于预测儿科病例的护理升级.
- 这项研究解决了基于ML的临床决策支持对于儿科社区获得性肺炎管理的需求.
研究的目的:
- 为初级保健医生开发一个强大的预测工具.
- 帮助确定最佳的患者管理和护理环境.
- 预测儿童社区获得性肺炎的护理升级的需要.
主要方法:
- 在COVID-19大流行之前,对437例小儿社区获得的肺炎病例进行了回顾性分析.
- 从非结构化记录中编码临床特征,使用儿童疾病综合管理指南.
- 应用合成少数群体过量采样技术-Tomek对不平衡数据的应用,Shapley对特征选择的附加解释,以及对模型优化进行组合的超参数调整.
主要成果:
- 优化模型在预测需要转移到更高的护理层次时达到77%~88%的准确性.
- 接收机操作员特征曲线 (AUC-ROC) 下的面积为0.88,精度回忆曲线 (AUC-PR) 下的面积为0.96.
- 确定的主要预测因素包括缺氧,呼吸困难,年龄,体重与年龄的z分数和投诉持续时间,独立于实验室诊断.
结论:
- 机器学习技术可用于创建儿童肺炎的预后工具.
- 开发的工具可以提前识别需要升级护理的病例.
- 这种方法将临床专业知识与数据科学相结合,以提高儿科肺炎管理.
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