基于机器学习的个性化生存预测模型用于骨髓瘤的预后:来自SEER数据库的数据
Ping Cao1, Yixin Dun2, Xi Xiang1
1Department of Orthopedic, The Frist Affiliated Hospital of Dalian Medical University, Dalian, China.
Medicine
|September 27, 2024
概括
与传统方法相比,机器学习模型,特别是DeepSurv,在预测骨髓瘤患者存活率方面表现出卓越的准确性. 这些人工智能工具可以指导个性化治疗决策,以获得更好的患者结果.
科学领域:
- 在瘤学瘤学.
- 生物统计学 生物统计学
- 机器学习在医学中的应用
背景情况:
- 由于瘤异质性和治疗方法的多样性,骨髓瘤患者的治疗结果是可变的.
- 准确的预后预测对于骨髓瘤管理中的有效临床决策至关重要.
研究的目的:
- 为了比较机器学习 (ML) 模型的预测性能与Cox比例危险 (CoxPH) 模型的骨髓瘤生存率.
- 探索ML模型在为骨髓瘤患者提供个性化治疗建议方面的实用性.
主要方法:
- 利用了来自SEER数据库的1243名骨髓瘤患者 (2000-2018) 的数据.
- 开发并验证了DeepSurv,NMTLR和RSF ML模型与CoxPH模型和TNM分期对比.
- 使用一致性指数 (C指数),综合障碍得分 (IBS),ROC曲线,AUC,校准和决策曲线分析评估模型性能.
主要成果:
- DeepSurv模型显示出最高的性能 (C指数:0.77,3年AUC:0.80,5年AUC:0.78),优于其他ML模型和CoxPH.
- 根据DeepSurv模型推的治疗对齐显示出明显更好的生存结果 (HR:1.88,P <.05).
- 对于DeepSurv模型的Web应用程序是公开可访问的,用于临床使用.
结论:
- 机器学习模型,特别是DeepSurv,在预测骨髓瘤存活率方面提供了更高的准确性.
- 这些ML模型可以为优化临床决策和个性化治疗策略提供有价值的见解.
- 开发的DeepSurv模型及其Web应用程序为改善骨髓瘤患者护理提供了实用工具.
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