在静脉血栓塞栓症患者的出血状态的机器学习分析
Soroush Shahryari Fard1, Theodore J Perkins1,2, Philip S Wells1,3
1The Ottawa Hospital Research Institute, the Ottawa Hospital, Ottawa, Ontario, Canada.
概括
与现有的临床模型相比,机器学习模型没有改善静脉血栓塞栓症 (VTE) 患者的出血风险预测. 基线数据的相似性限制了机器学习方法在这个患者群体中的有效性.
科学领域:
- 医疗信息学 医疗信息学
- 心脏病学 心脏病学
- 血液学 血液学 血液学
背景情况:
- 抗凝药对于管理静脉血栓栓塞 (VTE) 非常重要.
- 决定延长抗凝治疗需要平衡出血风险与VTE复发的风险.
- 现有的用于预测出血风险的统计模型存在局限性.
研究的目的:
- 评估机器学习 (ML) 算法在预测静脉瘤患者出血风险方面的有效性.
- 探索基线患者数据,以获得有关出血事件的新见解.
主要方法:
- 从2542名接受延长抗凝治疗的VTE患者收集了基线临床,人口和基因型数据.
- 应用无监督的ML算法用于数据可视化和分类出血与非出血组.
- 通过交叉验证,训练并将八个监督的ML算法与已建立的临床模型进行比较.
主要成果:
- 基线数据显示,经历出血和没有出血的患者之间存在很高的相似性.
- 根据VTE类型 (肺栓塞或深静脉血栓症),在出血者中确定了两个群体,但没有显著的风险差异.
- 监督的ML模型和临床模型显示了可比的预测性能 (c-统计~62%,布赖尔得分~0.045).
结论:
- 基线临床变量不足以提高超越当前模型的出血预测.
- 需要新的策略或数据模式来改善VTE患者的出血风险评估.
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