对接受化疗的晚期癌症患者的息治疗需求的预测模型
Arisa Kawashima1, Taiki Furukawa2, Takahiro Imaizumi3
1Division of Integrated Health Sciences (A.K. K.S.), Department of Nursing for Advanced Practice, Nagoya University Graduate School of Medicine, Nagoya, Japan; Department of Social Science (A.K.), Center for Gerontology and Social Science, Research Institute, National Center for Geriatrics and Gerontology, Obu, Japan..
Journal of pain and symptom management
|January 13, 2024
概括
机器学习模型可以预测接受化疗的晚期癌症患者的息护理需求. 使用五个关键变量 (包括疼痛分数) 的模型显示,它有望取代早期干预的传统查工具.
科学领域:
- 在瘤学瘤学.
- 抚慰性护理是一种缓解性护理.
- 医疗保健中的机器学习
背景情况:
- 对于晚期癌症患者来说,早期的息护理至关重要,理想情况下在诊断后8周内.
- 目前的指导方针建议对息护理需求进行例行查,但由于人员和时间限制,实施受到阻碍.
- 识别需要专业息护理的患者对于及时有效的支持至关重要.
研究的目的:
- 开发和评估机器学习 (ML) 模型,用于预测在接受化疗的晚期癌症患者中需要专业息治疗的需求.
- 评估这些ML模型作为当前选工具的替代品的潜力.
- 提高息治疗转诊的效率和及时性.
主要方法:
- 使用监督机器学习 (XGBoost算法) 进行了一项回顾性队列研究.
- 该研究包括在日本 (2018-2023) 接受化疗的成年患者 (≥18岁) 患有转移性或IV期癌症.
- 通过应急查得分和专家临床判断确定了息护理需求,利用来自癌症登记,索赔和护理记录的数据.
主要成果:
- 该研究分析了561名晚期癌症患者,其中114名 (20.3%) 被确定需要专业的息治疗.
- 在解决数据不平衡后,开发的ML模型实现了0.89的曲线下面面积 (AUC),具有高灵敏度 (95.8%) 和特异性 (71.9%).
- 一个使用五个关键变量的精细模型,包括患者报告的疼痛评分,显示AUC为0.82.
结论:
- 机器学习模型可以有效地预测正在接受化疗的晚期癌症患者的专业息护理需求.
- 使用五个预测变量的简化模型显示了替代现有选工具的潜力.
- 这种方法可以促进早期识别和获得息护理服务,改善患者的治疗结果.
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