使用机器学习和血液常规分析预测深静脉血栓形成
Jie Su1,2, Yuechao Tang3, Yanan Wang4
1Medical Neurobiology Laboratory, Inner Mongolia Medical University, Hohhot, China.
Frontiers in big data
|October 22, 2025
概括
使用常规血液检查的机器学习模型可以有效预测深静脉血栓塞 (DVT) 风险. 这种方法有助于早期的DVT诊断,改善患者的治疗结果,而不会增加医疗负担.
科学领域:
- 医学诊断 医学诊断 医学诊断
- 医疗保健中的机器学习
- 血液学 血液学 血液学
背景情况:
- 深静脉血栓 (DVT) 是一个重要的健康问题,具有严重的并发症,如肺栓塞和潜在的肢体截肢.
- 早期诊断DVT对于预防危及生命的结果和长期残疾至关重要.
研究的目的:
- 开发和评估机器学习模型,用于早期深静脉血栓塞 (DVT) 诊断,使用常规血液分析.
- 通过先进的特征选择技术,确定可预测DVT风险的关键血液指数.
主要方法:
- 对658名DVT患者和1418名健康对照者的回顾性分析 (2022年1月 - 2023年6月).
- SHAP (夏普利添加式扩展) 分析用于识别显著的血液常规指数.
- 构建和评估六个机器学习模型 (kNN,LR,DT,RF,SVM,ANN) 使用选定的特征,通过AUC进行评估.
主要成果:
- SHAP分析确定了十个关键的血液常规指数,用于DVT的预测.
- 所有六种机器学习模型都表现出强大的预测性能 (AUC>0.8,准确度>70%,灵敏度/特异性>70%).
- 随机森林 (RF) 模型在评估DVT风险方面表现最高.
结论:
- 使用常规血液检测的机器学习模型为早期DVT风险预测提供了一个有希望的途径.
- 这些模型可以促进及时诊断和干预,潜在地减少患者的发病率和死亡率.
- 建议进一步验证和改进以提高临床适用性和整合到常规实践中.
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