机器学习可以预测食道癌的治愈性多学科团队治疗决策
Navamayooran Thavanesan1, Indu Bodala2, Zoë Walters1
1School of Cancer Sciences, Faculty of Medicine, University of Southampton, UK.
概括
机器学习模型可以预测食道癌治疗决策,年龄被确定为关键因素. 这种方法旨在规范护理,减少癌症治疗计划中的健康不平等.
科学领域:
- 在瘤学瘤学.
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
背景情况:
- 消化管癌 (OC) 多学科团队 (MDTs) 的工作流压力导致决策变化和健康不平等.
- 机器学习 (ML) 提出了一个潜在的自动化,数据驱动的解决方案,以提高一致性和标准化OC护理.
- 现有的决策过程可能受到潜意识的偏见的影响,需要客观的分析工具.
研究的目的:
- 开发和评估ML模型,以预测治愈性OCMDT治疗决策.
- 识别和排名影响这些治疗决策的变量的重要性.
- 探索可解释的人工智能的潜力,揭示癌症护理决策中的偏见.
主要方法:
- 对399名消化管切除术患者 (2010-2020年) 的回顾性分析,这些患者接受了与或没有新辅助化疗 (NACT) 或化疗放射治疗 (NACRT) 的手术.
- 训练和比较ML算法:多项逻辑回归 (MLR),随机森林 (RF),极端梯度增强 (XGB) 和决策树 (DT).
- 使用诸如曲线下面面积 (AUC),精度,卡帕,LogLoss,F1和精度回忆AUC等指标评估模型性能;进行了变量重要性分析.
主要成果:
- 与RF,XGB和DT相比,MLR在关键指标 (平均AUC0.793) 中表现优异.
- 变量重要性分析始终确定患者年龄是影响单独手术和新辅助治疗之间的决定的重要因素.
- 该研究成功地使用有限的特征集开发了预测模型,年龄是关键决定因素.
结论:
- ML技术,特别是MLR技术,可以有效地预测食道癌症治疗决策.
- 可解释的人工智能为决策关键变量提供了有价值的见解,可能揭示MDT决策中的潜意识偏见.
- 这些数据驱动型号为优先考虑病例量,提高效率和协助MDT在标准化癌症护理方面提供了未来的潜力.
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