使用机器学习和自然语言处理精确和可扩展的分类结肠镜新生病
Brendan Broderick1, Jason Greenwood2, Douglas Mahoney3
1Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, Minnesota, USA.
Clinical and translational gastroenterology
|December 9, 2025
概括
一个机器学习模型从电子健康记录中准确地分类结直肠瘤,改善结直肠镜质量监测. 这种自然语言处理系统增强了腺瘤和状病变的检测.
科学领域:
- 医疗信息学医学信息学
- 机器学习在医疗保健中的应用
- 胃肠道学和瘤学
背景情况:
- 结肠直肠癌 (CRC) 是美国癌症死亡的主要原因.
- 结肠镜是预防CRC的主要查方法.
- 从内镜和病理学来准确识别结直肠瘤对于质量评估至关重要.
研究的目的:
- 评估随机森林机器学习模型用于分类结直肠瘤的可行性.
- 开发一种自然语言处理 (NLP) 系统来对电子健康记录中的发现进行分类.
主要方法:
- 在一个大型学术机构进行了一项回顾性队列研究.
- 开发了一个基于规则的算法,以从内镜和病理学数据中对瘤进行分类.
- 一个随机的森林NLP系统被训练在非结构化的病理发现和验证在一个独立的集.
主要成果:
- 该模型接受了35953份病理学报告和95188份结肠镜报告的训练.
- 在一个独立的验证集上,NLP系统实现了高精度.
- 曲线下的面积 (AUC) 值为腺瘤的0.997,状的0.99和晚期病变的0.99.
结论:
- 一个基于森林的随机NLP系统准确且可解释地对结肠镜检查结果进行分类.
- 与机器学习相结合的NLP为结肠镜质量监测提供了一个可扩展的策略.
- 这种方法可以提高瘤检测的准确性和患者的治疗结果.
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