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Updated: Mar 15, 2026

Robust Ligature-Induced Model of Murine Periodontitis for the Evaluation of Oral Neutrophils
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针对严重牙周炎的选择性机器学习算法,根据问卷数据进行标记.

E Stamatelou1, N Nijland1, N Su2

  • 1Department of Periodontology, Academic Center for Dentistry Amsterdam (ACTA), University of Amsterdam and Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.

Scientific reports
|March 14, 2026
PubMed
概括
此摘要是机器生成的。

一个机器学习管道准确地识别严重牙周炎 (SP) 和没有牙周炎 (NP),使用自我报告的口腔健康问卷. 这种方法有助于流行病学研究缺乏临床数据的病例控制选择.

关键词:
在 CatBoost 中使用 CatBoost.可解释的人工智能梯度增加了树木的增长.标签噪声 标签噪声牙周病是牙周病的一种疾病.自己报告的口腔健康状况.

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相关实验视频

Last Updated: Mar 15, 2026

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科学领域:

  • 口腔健康研究 口腔健康研究
  • 流行病学 流行病学
  • 机器学习应用程序 机器学习应用程序

背景情况:

  • 流行病学研究经常收集自我报告的口腔健康 (SROH) 数据,但缺乏临床牙周测量.
  • 准确识别牙周炎病例对于队列研究至关重要,但在没有临床检查的情况下具有挑战性.

研究的目的:

  • 开发和验证机器学习 (ML) 管道,从SROH问卷中选择性识别严重牙周炎 (SP) 和无牙周炎 (NP).
  • 支持在大型队列中的病例控制选择,在没有临床牙周病理数据的情况下.

主要方法:

  • 使用CatBoost在三个数据集 (n=498) 上开发了一个两阶段的ML管道,其中包括SROH,人口统计和CPITN得分.
  • 该管道涉及数据清理,特征工程和两种模型方法 (分离器-A和分离器-Z) 具有高可信度预测值 (≥0.85).
  • 模型性能在内部测试和保留推断集上进行了评估,重点是准确分类NP和SP病例,同时排除中度牙周炎 (MP).

主要成果:

  • ML管道在高可信度子集 (4.31%的合格病例) 中实现了NP和SP病例的完全分离.
  • 没有中度牙周炎 (MP) 病例被错误分类为NP或SP.
  • 该管道在选择性地从问卷数据中识别牙周炎状态方面显示出高性能和可解释性.

结论:

  • 选择性,可解释的两阶段ML管道可以从SROH问卷数据中准确识别严重牙周炎和无牙周炎.
  • 这种方法在缺乏临床牙周检查的流行病学队列中为病例控制选择提供了有价值的工具.
  • 建议进行进一步的验证,以确认管道在不同人群中的通用性.