机器学习算法用于识别痴呆症诊断后死亡风险的预测变量:一个纵向队列研究
Shayan Mostafaei1,2, Minh Tuan Hoang3,4, Pol Grau Jurado3
1Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institute, Stockholm, Sweden. shayan.mostafaei@ki.se.
Scientific reports
|June 10, 2023
概括
机器学习模型确定了痴呆症患者的关键死亡风险因素. 这些算法提高了对痴呆症预后的理解,并可以帮助临床决策.
科学领域:
- 计算神经科学是一种神经科学.
- 老年医学 老年医学
- 生物统计学 生物统计学
背景情况:
- 传统的统计模型在识别痴呆症死亡率的复杂风险因素方面存在局限性.
- 机器学习 (ML) 为分析大型数据集和发现新兴关联提供了潜在的优势.
研究的目的:
- 利用ML算法来识别大量痴呆症患者中死亡率的显著预测因素.
- 将ML模型的性能与死亡风险预测的传统方法进行比较.
主要方法:
- 应用稀疏性诱导的ML算法 (例如,支持矢量机器,CoxBoost) 对来自瑞典认知/痴呆障碍登记处 (SveDem) 的28,023名痴呆患者的队列.
- 评估了60个潜在的预测变量,包括人口统计,临床评估 (例如,迷你精神状态检查),诊断过程时间和并发症.
- 使用ROC曲线下的面积 (AUROC) 来评估分类性能和无监督的集群来确定患者组.
主要成果:
- 机器学习算法确定了20个重要变量用于死亡风险分类和15个用于死亡时间预测.
- 关键预测因素包括诊断时的年龄,迷你精神状态检查得分,性别,体重指数和查尔森并发症指数.
- 诊断过程指标,例如从转诊到工作启动的时间,成为重要因素.
- 在死亡风险分类方面,Support Vector Machines获得了0.7375的AUROC.
结论:
- 稀疏性诱导的ML算法有效地识别了痴呆症的关键死亡风险因素,补充了传统的统计方法.
- 这些发现提高了对痴呆症预后的理解,并突出了临床风险分层中ML的潜力.
- 确定了与痴呆症死亡率相关的新风险因素,需要进一步调查.
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