机器学习可以在癌症治疗期间分配息治疗咨询
Jiang Chen He1,2, Gordon Taylor Moffat1, Sho Podolsky3
1Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada.
概括
预后机器学习系统可以增加晚期癌症患者早期息治疗 (PC) 的机会,改善结果. 这种人工智能工具有助于克服PC服务的容量限制,确保更多的患者获得及时支持.
科学领域:
- 在瘤学瘤学.
- 医疗信息学 医疗信息学
- 抚慰性护理是一种缓解性护理.
背景情况:
- 早期息治疗 (PC) 改善了生活质量,生存率,并降低了晚期癌症患者的成本.
- 现有的容量限制阻碍了所有符合条件的患者及时进行PC咨询.
- 需要预测工具来优化PC资源配置.
研究的目的:
- 评估预后机器学习系统是否可以促进早期息护理 (PC) 咨询.
- 评估系统对现有医疗保健能力内的PC接入的影响.
主要方法:
- 使用加拿大安大略省的人口级行政数据开发了一个机器学习系统.
- 该系统预测了接受息意图治疗的不可治愈癌症患者的1年死亡风险.
- 通过将预测结果与通常的护理进行比较,评估了该系统对PC的潜在影响.
主要成果:
- 机器学习系统的预测值为69.7%,预测1年死亡率的灵敏度为74.9%.
- 系统指导护理可以使早期PC总体增加8.5%,在治疗后6个月生存的患者中增加15.3%.
- 这一增长没有增加PC咨询总数或显著影响2年以上预后的患者.
结论:
- 预测机器学习系统在增加早期PC访问方面表现有前途,尽管资源有限.
- 这些系统的实时部署和评估对于改善患者护理至关重要.
- 人工智能驱动的预后工具可以优化晚期癌症患者的息护理服务.
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