[公式:查看文本]:可解释的注意力转换器,用于识别影响牙科访问的因素,以提高牙科数据的完整性
Veena Mayya1,2, Giang T Vu3, Babu Mandhidi4
1Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India.
BMC oral health
|December 17, 2025
概括
了解患者的行为是改善牙科护理准入的关键. 这项研究使用机器学习来识别影响每年去看牙医的因素,帮助有针对性的公共卫生干预,以获得更好的口腔健康结果.
科学领域:
- 公共卫生 公共卫生
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
背景情况:
- 常规的牙科护理对于预防性医疗保健至关重要,早期识别问题以预防晚期牙科疾病.
- 定期检查可以减少对侵入性治疗的需要,并最大限度地减少并发症.
- 确定患者行为的关键决定因素对于改善牙科护理准入至关重要.
研究的目的:
- 确定影响患者对牙科护理行为的关键决定因素.
- 通过使用先进的分析方法,发现影响每年牙医诊所 (ADV) 的因素.
主要方法:
- 利用了2022年行为风险因素监测系统 (BRFSS) 数据集,用于成人健康行为和获得护理.
- 提出了一种混合方法,将使用变压器模型与机器学习 (ML) 模型的特征选择结合起来.
- 评估了用于特征选择的变压器架构 (RoBERTa,ELECTRA,BERT) 和用于预测ADV的ML模型 (CatBoost,XGBoost,决策树).
主要成果:
- 像RoBERTa,ELECTRA和BERT这样的变压器模型在特征选择中表现出高性能.
- CatBoost和XGBoost在每年牙科诊所的预测准确率最高,分别为76.0%和75.6%.
- 提出的方法有效地减少了特征空间,改善了焦点,减少了训练/推断时间,而不会影响准确性.
结论:
- 基于融合的模型为医疗保健提供者提供了宝贵的见解,以开发有针对性的干预措施.
- 了解导致牙科不定期诊断的因素可以指导基于证据的策略来克服障碍.
- 更好地了解决定因素可以带来更好的口腔健康结果,并增加获得预防性牙科服务的机会.
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