可解释的机器学习模型用于预测喉癌的总生存率
Rasheed Omobolaji Alabi1,2, Alhadi Almangush1,3,4, Mohammed Elmusrati2
1Research Program in Systems Oncology, University of Helsinki, Helsinki, Finland.
Acta oto-laryngologica
|January 27, 2024
概括
与DeepTables相比,最先进的机器学习算法在预测喉状细胞癌 (LSCC) 患者的整体存活率方面表现优异. 影响生存的关键因素包括年龄,瘤阶段和年级.
科学领域:
- 在瘤学瘤学.
- 机器学习在医学中的应用
- 癌症预后 癌症预后
背景情况:
- 近几十年来,喉状细胞癌 (LSCC) 的死亡率一直停滞不前.
- 准确预测整体存活率 (OS) 对于有效的LSCC患者管理至关重要.
- 现有的预测模型可能会从先进的机器学习方法中受益.
研究的目的:
- 为了比较DeepTables的预测性能与LSCC整体生存 (OS) 分层的最先进的机器学习 (ML) 算法.
- 通过使用全球和本地模型不可知的技术来提高模型的解释性.
- 在LSCC患者中确定OS的关键预测因子.
主要方法:
- 来自监测,流行病学和最终结果 (SEER) 数据库的2792名LSCC患者的分析.
- 深度表与整体ML算法 (投票,堆,XGBoost) 的比较,用于OS预测.
- 应用夏普利添加式解释 (SHAP) 来实现全局可解释性和局部可解释模型不可知解释 (LIME) 来实现局部可解释性.
主要成果:
- 最先进的ML组合算法在预测LSCC患者存活率方面超过了DeepTables.
- 集成算法在接收曲线 (AUC) 下实现了可比的加权面积,范围从76.1到76.9,精度在70.2%到71.8%之间.
- SHAP分析确定了患者在诊断时的年龄,N阶段,T阶段,瘤等级和婚姻状况是主要的预测因素.
结论:
- 机器学习模型显示了预测LSCC患者的整体存活率的巨大潜力.
- 这些ML模型可以作为有价值的辅助工具,以帮助LSCC的治疗计划.
- 可解释性方法提供了对LSCC生存的关键驱动因素的见解.
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