用于在资源有限的设施中使用的儿科智能分拣模型的外部验证
Joyce Kigo1, Stephen Kamau1, Alishah Mawji2
1Health Service Unit, Kenya Medical Research Institute (KEMRI)-Wellcome Trust Research Programme, Nairobi, Kenya.
PLOS digital health
|June 21, 2024
概括
儿科紧急护理的智能分辨模型在肯尼亚显示出很好的准确性,验证了其在新环境中的使用. 再校准改善了它的适合性,但没有改变患者的优先级,这表明它具有广泛的适用性.
科学领域:
- 儿科急救医学 儿科急救医学
- 医疗信息学 医疗信息学
- 临床决策支持系统 临床决策支持系统
背景情况:
- 数字分拣模型对于资源有限的急救部门至关重要.
- 外部验证对于确保这些模型的通用性至关重要.
- 在乌干达开发的智能选模型需要在不同的环境中进行验证.
研究的目的:
- 在肯尼亚医院外部验证九预测器智能选儿科模型.
- 为了评估模型的歧视和校准.
- 评估重新校准对模型性能和患者优先级的影响.
主要方法:
- 利用两个肯尼亚医院5003名儿童的数据.
- 评估模型使用接收机-运营商曲线 (AUC) 下的面积进行歧视.
- 通过优化对分类类别的拦截来评估校准并执行重新校准.
主要成果:
- 智能分辨模型显示出良好的歧视 (AUC从0.784到0.826不等).
- 预校准的值显示高灵敏度 (81%-93%) 和特异性 (86%-96%).
- 再校准改进了图形适合性,但需要新的特定地点的风险值.
结论:
- 智能分拣模型是多样化,资源有限的环境中儿科分拣的一个有前途的工具.
- 外部验证证实了良好的区分,重新校准提高了校准适度.
- 该模型保持患者优先顺序的能力对于广泛采用是有价值的.
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