基于机器学习的预测模型对每年监测内镜检测胃癌的成本的影响
Junya Arai1,2, Atsushi Miyawaki3,4, Yoku Hayakawa2
1Division of Gastroenterology Asahi Life Foundation, Tokyo, Japan.
iGIE : innovation, investigation and insights
|February 6, 2026
概括
机器学习 (ML) 模型可以降低用于胃癌 (GC) 检测的年度内镜成本. 这种ML方法提高了成本效益,并保持了高风险患者高GC检测率.
科学领域:
- 在瘤学瘤学.
- 医疗信息学 医疗信息学
- 胃肠病学 胃肠病学
背景情况:
- 胃癌 (GC) 监测依赖于内镜检查,造成了相当大的成本.
- 有效的风险分层对于优化监控策略至关重要.
- 像OLGA和OLGIM这样的现有方法可能不完全具有成本效益.
研究的目的:
- 评估基于机器学习 (ML) 的模型,以降低GC监控的成本.
- 将ML模型的性能与现有的风险分层方法进行比较.
- 评估ML对所需查数量 (NNS) 和每个检测到的GC的成本的影响.
主要方法:
- 在1099名患有慢性胃炎的患者身上开发了一种使用梯度增强决策树的ML模型.
- 随机分配患者参加培训 (4:1) 和测试组.
- 基于NNS,成本和GC检测率的ML驱动风险分层与OLGA和OLGIM进行了比较.
主要成果:
- 通过ML选择的高风险队列显示NNS和总成本明显较低.
- 与其他方法相比,ML模型实现了较低的每次检测GC成本.
- 在ML识别的高风险组中,高GC检测率保持不变.
结论:
- 开发的ML模型显示了降低GC监控成本的潜力.
- 这种方法保持了胃癌检测的高诊断准确度.
- 基于ML的风险分层为内镜监测提供了一个有希望的,具有成本效益的替代方案.
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