使用算法风险评估预测老年慢性病患者的死亡率:东南亚的观点
Bernard Tiang Guan Koh1, Srinath Sridharan1, Narayan Venkataraman1
1Data Science and Intelligence, Changi General Hospital, Singapore, Singapore.
BMC nephrology
|August 26, 2025
概括
机器学习模型在预测开始血液透析的老年患者的六个月死亡率方面表现有前途. 这些先进的工具提供了更好的预后洞察力,以帮助慢性病管理的临床决策.
科学领域:
- 肝脏病学
- 老年病学
- 数据科学
- 人工智能
背景情况:
- 慢性病 (CKD) 在老年患者中的全球负担正在增加,这给治疗带来了重大挑战.
- 老年患者在开始透析后经常出现衰退,使治疗决策复杂化.
- 目前的预后模型在概括性和捕捉复杂的患者因素方面存在局限性.
研究的目的:
- 开发和评估机器学习 (ML) 模型,用于预测开始血液透析的老年患者的六个月死亡率.
- 将ML模型与传统预测工具的性能进行比较.
- 通过SHapley添加式解释 (SHAP) 确定死亡率的关键预测因素.
主要方法:
- 开发随机森林和平衡的随机森林ML模型.
- 使用了一个单中心队列,包括1606名晚年患者 (≥65岁) 患有晚期慢性脏病,开始进行血液透析.
- 将ML模型性能 (ROC-AUC) 与现有的预测工具进行比较,并将特征重要性纳入SHAP.
主要成果:
- 正常随机森林模型的ROC-AUC为0.83,略高于平衡随机森林模型 (0.82).
- 与传统的预测工具相比,这两种ML模型都表现出优异的性能和校准性.
- SHAP分析提供了模型可解释性的特征重要性排名.
结论:
- 基于ML的模型在预测老年终端病患者的六个月死亡率方面提供了适度的改善.
- 这些预测工具可以在共享决策中补充临床判断.
- 机器学习模型提供了有价值的预后见解,以支持,而不是取代临床专业知识.
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