机器学习模型可以预测在家庭住院的晚期癌症患者的6个月死亡风险
Wan Cheng1, Jianwei Zheng2, Yuanfeng Lu1
1School of Nursing, Fujian Medical University, Fuzhou, China.
Asia-Pacific journal of oncology nursing
|April 15, 2025
概括
使用机器学习,可以预测晚期癌症宿舍患者的6个月死亡率. 使用常规家庭访问数据的物流回归模型可以有效地识别针对性临终关怀的高风险个人.
科学领域:
- 在瘤学瘤学.
- 抚慰性护理是一种缓解性护理.
- 医疗信息学 医疗信息学
背景情况:
- 接受家庭临终关怀的晚期癌症患者面临着显著的6个月死亡风险.
- 准确预测死亡风险对于及时有效的护理计划至关重要.
研究的目的:
- 开发和比较机器学习模型,以预测晚期癌症在家庭住院患者的6个月死亡风险.
- 确定死亡率的关键预测因素,并为风险评估创建临床工具.
主要方法:
- 一项回顾性预后研究,涉及7023名患有晚期癌症的家庭住院患者.
- 使用了五种机器学习算法 (逻辑回归,随机森林,XGBoost,支持矢量机,神经网络).
- 用灵敏度,特异性,准确性,AUC和F1评分来评估模型性能;开发了一个名图.
主要成果:
- 后勤回归模型表现出最佳性能,AUC为0.754 (95% CI:0.721-0.786).
- 从这个模型中得出的名图识别出了6个月死亡率的10个独立风险因素.
- 该模型显示了良好的校准,并在临床决策曲线分析中提供了显著的净收益.
结论:
- 从第一次家庭访问中例行收集的医疗保健数据可以用来预测死亡风险.
- 这种预测能力可以帮助查高风险患者,告知有针对性的宿舍护理策略.
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