$$\mathcal {EAT}$$ : explainable attentive transformers for identifying the factors influencing dental visits to enhance dental data completeness
Our proposed method effectively reduced the feature space, thereby improving focus and reducing training and inference time without compromising accuracy. This fusion-based model provides valuable insights for healthcare providers, enabling the development of targeted interventions tailored to specific population needs. Understanding the factors contributing to irregular dental visits can guide evidence-based strategies to overcome barriers and improve overall oral health outcomes.
Authors
- Christian King (ORCID: https://orcid.org/0000-0003-1157-5734)
- Varadraj P. Gurupur (ORCID: https://orcid.org/0000-0001-5723-7998)
- Bertis B. Little (ORCID: https://orcid.org/0000-0002-6485-4641)
- Astha Singhal (ORCID: https://orcid.org/0000-0001-9191-6978)
- Veena Mayya (ORCID: https://orcid.org/0000-0002-8091-5053)
- Giang T. Vu (ORCID: https://orcid.org/0000-0003-1159-6973)
- Babu Mandhidi
Institutions
- University of Central Florida (US)
- University of Louisville (US)
- Manipal Academy of Higher Education (IN)
- University of Florida (US)
Publication Details
- Journal
- BMC Oral Health
- Published
- 2025-12-17
- DOI
- https://doi.org/10.1186/s12903-025-07170-0
- Citations
- 1
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 2.48