机器学习模型用于常规检测"完整血液计数管中的错误血液"错误
Christopher-John Farrell1, Charles Makuni2, Aaron Keenan2
1Clinical Chemistry Department, NSW Health Pathology-Liverpool Hospital, Sydney, Australia.
Clinical chemistry
|July 20, 2023
概括
一个新的机器学习模型有效地检测了当前实验室程序错过的输血错误 (WBIT). 实验室诊断的这一进步通过改善WBIT错误识别来提高患者的安全性.
科学领域:
- 临床诊断 临床诊断 临床诊断
- 医学实验室科学 医学实验室科学
- 医疗保健中的人工智能
背景情况:
- 目前的实验室方法可能无法检测出错误的输血管 (WBIT) 错误.
- 现有的机器学习模型对WBIT检测有局限性,包括处理缺失数据和低正预测值 (PPV).
- 需要一种适合常规临床实验室使用的机器学习模型.
研究的目的:
- 开发和评估一种机器学习模型,用于常规检测输入管中的错误血液 (WBIT) 错误.
- 评估模型能够识别传统实验室程序未能识别的WBIT错误的能力.
- 确定开发的机器学习模型的正预测值 (PPV).
主要方法:
- 一个机器学习模型被训练在135,128个完整血清 (CBC) 结果的回顾性数据集上.
- 该模型在22周内前性地应用于例行实验室样本.
- 由模型标记的样本进行了进一步的调查,包括血液组和红细胞表型测试.
主要成果:
- 该模型被前性地应用于38187个通过常规检查的CBC结果.
- 确定了110个样本进行进一步测试,导致检测到12个错误的血液在管 (WBIT) 错误.
- 机器学习模型的正预测值 (PPV) 为10.9%.
结论:
- 适合常规使用的机器学习模型可以识别当前实验室程序遗漏的输血错误 (WBIT) 错误.
- 机器学习为提高临床实验室中WBIT错误检测提供了一个有价值的工具.
- 这些模型的验证和部署可以显著提高患者的安全性.
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