开发和验证机器学习驱动的风险指数,以预测患者在转诊,评估和移植等待列表期间的退学情况
Solaf Al Awadhi1, Enshuo Hsu2, Thomas B H Potter2
1Department of Surgery, Houston Methodist, Houston, Texas, USA.
Clinical transplantation
|September 19, 2025
概括
机器学习模型可以在评估和等待列表过程中识别高风险的移植候选人. 早期识别这些患者可以帮助减少差异,改善移植机会.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學.
- 移植手术 移植手术
- 健康差异 在健康上的差异
- 医疗保健中的机器学习
背景情况:
- 移植是衰竭的最佳治疗方法,但接入差异仍然存在.
- 现有的国家注册表无法捕捉移植过程中的早期脱落阶段.
- 开发了风险指数,以预测在转诊,评估和等待列表中的学.
研究的目的:
- 开发和验证风险指数,用于预测移植候选人的早期学.
- 在移植过程的不同阶段,确定与更高脱学风险相关的患者特征.
- 为了能够及早识别有风险的患者,进行有针对性的干预.
主要方法:
- 利用机器学习 (ML) 模型预测移植候选人的学风险.
- 包括来自电子健康记录和人口普查数据的人口,临床和社会经济变量.
- 使用接收器操作特征 (AUROC) 曲线下的面积来评估模型性能.
主要成果:
- 46%的转诊患者错过了第一次评估,54%的符合条件的患者没有被列入等待名单,31%的等待名单上的患者变得不活跃.
- 风险较高的患者是年龄较大,肥胖,社会经济不利,属于少数民族.
- 特定阶段的风险因素包括转诊时的社会决定因素,评估时的并发症,以及等待列表时的数字排斥.
结论:
- ML模型在多个阶段有效地识别了移植中断风险.
- 通过这些模型,可以早期识别有风险的患者.
- 有针对性的干预措施可能会减少差距,改善移植机会.
相关概念视频
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