通过对推信的数据驱动查,通过人工智能增强的分拣来改善肌肉骨护理
Tjardo Daniël Maarseveen1, Herman Kasper Glas2, Josien Veris-van Dieren2
1Department of Rheumatology, Leiden University Medical Center, Leiden, Zuid-Holland, the Netherlands.
NPJ digital medicine
|February 13, 2025
概括
机器学习模型可以有效地优先考虑类风湿病的转诊,识别类风湿性关节炎和其他疾病. 这种人工智能方法提高了效率,并加快了肌肉骨疾病患者的专业护理.
科学领域:
- 类风湿病学 类风湿病学
- 人工智能的人工智能
- 临床信息学 临床信息学
背景情况:
- 肌肉骨的投诉占全科医生咨询的30%.
- 许多患者通过信件被转介到类风湿病诊所,这给优先级设置带来了挑战.
研究的目的:
- 开发和验证机器学习 (ML) 管道,以优先考虑类风湿病转诊.
- 确定包括类风湿性关节炎 (RA),骨关节炎和纤维肌痛在内的特定疾病,以及需要长期护理的患者.
主要方法:
- 利用了来自12家诊所的5,728名患者的8044封推信.
- 在两个大型中心训练并验证了ML模型,在另外十个中心评估了通用性.
- 使用接收器操作特征曲线下的面积 (AUC-ROC) 评估模型性能.
主要成果:
- RA 分类器实现了0.78的AUC-ROC,骨关节炎0.71和纤维肌痛0.81.
- 与手动转诊系统相比,RA分类器显示RA病例的优先级优越 (P < 0.001).
- 其他分类器也显示了优先级的显著改善,提高了护理效率.
结论:
- 机器学习模型提供了一个强大的解决方案来优先考虑类风湿病转诊,优于传统方法.
- 这项技术有可能减少临床医生的工作量,并促进更早的专业护理.
- 未来的开发旨在将这些模型整合到临床决策支持工具中.
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