在使用机器学习的抗凝固癌症患者中开发静脉血栓栓塞复发的预测模型
Andres J Muñoz1, Juan Carlos Souto2, Ramón Lecumberri3
1Gregorio Marañón Health Research Institute, Complutense University, Madrid, Spain.
Thrombosis research
|June 22, 2023
概括
患有静脉血栓塞栓症 (VTE) 的癌症患者面临高复发风险. 这项研究开发了一种机器学习模型,可以在六个月内预测VTE复发,帮助高风险个体的临床管理.
科学领域:
- 在瘤学瘤学.
- 心脏病学 心脏病学
- 医疗信息学 医疗信息学
背景情况:
- 患有癌症和静脉血栓塞栓症 (VTE) 的患者尽管接受抗凝药治疗,但仍有明显的VTE复发风险.
- 早期识别高风险患者对于有效的临床管理和改善结果至关重要.
研究的目的:
- 开发和验证一个预测模型,以评估在6个月内在被诊断为VTE的癌症患者中VTE复发的风险.
- 通过使用机器学习,在这个患者群体中确定VTE复发的关键预测因素.
主要方法:
- 利用EHRead®技术与自然语言处理 (NLP) 和机器学习 (ML) 来提取来自西班牙9家医院 (2014-2018) 16407名癌症患者的电子健康记录的数据.
- 进行了临床和ML驱动的特征选择,以确定VTE复发的预测因素.
- 使用逻辑回归 (LR),决策树 (DT) 和随机森林 (RF) 算法进行训练和验证的预测模型.
主要成果:
- 总共分析了16,407名患有VTE的抗凝血癌症患者;在随访期间,11.4%的患者出现了复发性VTE.
- 确定的主要预测因素包括初级肺栓塞,深静脉血栓塞,转移,腺癌,血红蛋白和血清肌水平,血小板和白细胞计数,VTE家族病史和患者年龄.
- 随机森林模型实现AUC-ROC为0.68 (95% CI:0.63,0.72),表明预测性能中等.
结论:
- 这是第一个基于ML的预测模型,旨在预测癌症患者6个月VTE复发.
- 开发的模型可以帮助临床医生识别高风险患者,从而优化临床管理策略.
- 该模型的进一步验证和实施可能会导致改善患者护理和减少静脉瘤复发率.
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