基于机器学习的早期预警系统,用于在癌症系统治疗期间利用急性护理
Robert C Grant1,2,3, Jiang Chen He1,2, Ferhana Khan4
11Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.
Journal of the National Comprehensive Cancer Network : JNCCN
|October 19, 2023
概括
一个新的机器学习预警系统可以预测接受全身治疗的癌症患者急性护理需求. 该工具旨在通过识别高风险个体来改善预防干预和个性化癌症治疗.
科学领域:
- 在瘤学瘤学.
- 医疗信息学 医疗信息学
- 机器学习 机器学习
背景情况:
- 在癌症全身治疗期间,急诊室的访问和住院是常见的.
- 开发了一个纵向预警系统,以预测急性护理的利用率.
研究的目的:
- 开发和评估基于机器学习的预警系统,用于预测接受全身治疗的癌症患者急性护理的使用情况.
- 确定预测急性护理干预需要的关键特征.
主要方法:
- 分析了一组基于人口的回顾性队列,105,129名患者接受了针对非血液性癌症的静脉全身治疗.
- 预测模型包括静态 (人口统计,癌症类型) 和动态 (症状,实验室值) 特性.
- 该系统预测了治疗后30天内急性护理利用的概率.
主要成果:
- 开发的整体模型在测试队列中实现了0.742的接收器操作特征曲线下的面积.
- 关键预测因素包括先前的急性护理使用,治疗方案和实验室测试结果.
- 该系统能够标记大量急性护理事件,但对某些疗法和人群的低估值.
结论:
- 机器学习预警系统在识别急性护理利用风险的癌症患者方面表现有前途.
- 这些系统可以支持预防干预和量身定制的治疗策略.
- 需要进一步的研究来解决潜在的偏见,并前性地评估系统影响.
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