使用链接电子健康和牙科记录的牙周炎预测模型
J S Patel1, M Tellez1, R Katiyar1
1Center for Artificial Intelligence, Informatics, and Data Science, Department of Oral Health Sciences, Temple University Kornberg School of Dentistry, Philadelphia, PA, USA.
JDR clinical and translational research
|February 11, 2026
概括
整合医疗和牙科记录可以改善牙周病的预测. 牙科因素仍然是关键因素,但系统性疾病为早期干预和个性化护理提供了宝贵的见解.
科学领域:
- 口腔健康研究研究 口腔健康研究
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
背景情况:
- 牙周病 (PD) 与全身健康有关,但预测模型经常排除医疗数据.
- 目前仅关注牙科记录的现有模型限制了全面的风险评估.
研究的目的:
- 通过使用机器学习 (ML) 将电子牙科记录 (EDR) 与电子健康记录 (EHR) 整合起来,提高牙周病 (PD) 风险预测.
主要方法:
- 利用成年患者的EDR和EHR数据,将牙科和医疗信息联系起来.
- 开发了用于广泛的EHR数据预处理的自动化特征减少技术.
- 使用交叉验证进行训练和评估的ML模型 (高斯素朴贝叶斯,随机森林,LightGBM,XGBoost).
主要成果:
- 最好的模型使用了千平方选择的特征,实现了85%的特异性,67%的灵敏性和84%的AUC.
- 口腔卫生和吸烟状况等牙科因素是主要的预测因素.
- 系统性疾病 (心血管,内分泌,) 有意义地促进了PD风险预测.
结论:
- 系统健康和口腔健康之间观察到强烈的关联,尽管牙因素是主要预测因素.
- 整合医疗和牙科记录可以指导临床医生识别高风险患者进行早期干预.
- 人工智能驱动的模型显示了个性化护理和改进跨学科健康管理的潜力.
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