机器学习来预测放射性核素治疗服务的环境剂量率 - - 一项概念验证研究
Richard Meades1, Joanne Page1, James C Ross1
1The Nuclear Medicine Department, The Royal Free Hospital London NHS Foundation Trust, United Kingdom.
概括
机器学习 (ML) 可以使用患者预订数据预测放射性核素治疗废水管道附近的辐射剂量率. 这有助于未来的风险评估,改善辐射安全和合规性.
科学领域:
- 医学物理 医学物理
- 核医学是一种核医学.
- 数据科学数据科学数据科学
背景情况:
- 2017年"电离辐射法规"要求对辐射剂量进行风险评估和环境监测.
- 目前的方法面临局限性:风险评估依赖于假设,监测只提供后续数据.
- 准确的前性评估对核医学至关重要,特别是在放射性核素治疗患者废水管道周围.
研究的目的:
- 调查机器学习 (ML) 预测环境辐射剂量率的潜力.
- 用患者预订记录来计算核医学中潜在的风险评估.
- 评估ML模型在预测放射性核素治疗废水管道附近的剂量率方面的性能.
主要方法:
- 训练了六种不同的ML模型,使用540天的历史平均每日辐射剂量速率测量.
- 利用相应的部门治疗预订记录作为模型培训的输入特征.
- 分析了预测准确性,通过比较预测与测量的60天的每日平均剂量率.
主要成果:
- 一个梯度增强回归模型展示了最佳性能.
- 取得了1.10μSv.hr-1的根平均平方误差,0.87μSv.hr-1的平均绝对误差和0.411.2的R2.
- 确定了改善模型性能和在其他场景中潜在应用的方法.
结论:
- 机器学习可以有效地预测废水管道附近环境辐射剂量率的时间波动.
- 这种方法显示了提高立法合规性和辐射安全准确性的潜力.
- 机器学习应用可以优化核医学设施的员工时间和资源分配.
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