通过考虑模糊人口来评估静脉血栓栓塞的新风险评估模型
Xin Wang1,2, Yu-Qing Yang3, Xin-Yu Hong4,2
1Department of Ultrasound, Peking Union Medical College Hospital, Beijing, China.
BMC medical informatics and decision making
|December 30, 2024
概括
一种新的机器学习模型通过解决阶级失衡,有效地预测住院患者静脉血栓栓塞 (VTE) 风险. 与现有方法相比,这种方法提供了更好的准确性和稳定性,提高了临床风险评估.
科学领域:
- 医疗信息学 医疗信息学
- 机器学习 机器学习
- 临床风险评估临床风险评估
背景情况:
- 静脉血栓栓塞 (VTE) 对高风险住院患者构成重大健康和经济风险.
- 现有的用于预测VTE风险的机器学习 (ML) 模型通常由于阶级不平衡问题而失败,从而限制了它们的临床实用性.
- 广泛使用的帕多瓦模型虽然已经建立,但可能无法完全捕捉复杂的风险因素.
研究的目的:
- 为中国医疗住院患者量身定制的基于ML的新型VTE风险评估模型.
- 用"模糊人口"概念来解决和建模VTE预测中的阶级不平衡问题.
- 提高临床应用的VTE风险预测模型的性能和稳定性.
主要方法:
- 研究人员使用了来自北京联合医学院医院的3284名住院患者的数据集.
- 引入了一种新的"模糊人口"概念,以建模VTE预测中的阶级失衡问题.
- 为了构建新的ML VTE风险评估模型,采用了人口分割策略,并与帕多瓦模型和其他五个ML算法进行了比较.
主要成果:
- 与帕多瓦模型相比,拟议的模型表现出优异的特异性 (64.94%) 和同等的灵敏性 (90.24%).
- 开发的模型表现出更高的稳定性,与其他五种ML模型相比,灵敏度和特异性的标准偏差较小.
- 根据"模糊人口"概念调整负样本加剧了其他ML模型的不稳定性,突出了拟议模型的优势.
结论:
- 新的ML模型为住院患者的VTE风险评估提供了更好的灵敏度,特异性和稳定性.
- 这种基于人口分裂的ML方法有效地模拟了阶级不平衡,显示了在疾病风险评估中更广泛应用的潜力.
- 这项研究为临床VTE风险分层提供了更可靠的工具,可能会超过传统方法.
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