Identifying predictors of tooth loss using a rule‐based mach…
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- DOI 10.1002/jper.23-0030
- PMID https://pubmed.ncbi.nlm.nih.gov/37063053
- OpenAlex ID W4366083463
- Type Article
- Language EN
- Published 17 Apr 2023
- Volume 94 (10)
- Pages 1231–1242
- Source Journal of Periodontology
- Publisher Wiley
- FWCI 4.50
- Countries 1
- Institutions 1
- Added 19 Apr 2023
- Updated 01 Nov 2026
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Identifying predictors of tooth loss using a rule‐based machine learning approach: A retrospective study at university‐setting clinics
Article 2023 EN
BACKGROUND: This study aimed to identify predictors associated with tooth loss in a large periodontitis patient cohort in the university setting using the machine learning approach. METHODS: Information on periodontitis patients and 18 factors identified at the initial visit was extracted from electronic health records. A two-step machine learning pipeline was proposed to develop the tooth loss pr…
16
Citations
54
References
7
Authors
4.50
FWCI
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UN Sustainable Development Goals
1 SDGsAuthors & Affiliations
7 authors| # | Author | Institutions | ORCID |
|---|---|---|---|
| 1 | Chun‐Teh Lee | The University of Texas Health… (US) | — |
| 2 | Kai Zhang | The University of Texas Health… (US) | |
| 3 | Wen Li | The University of Texas Health… (US) | |
| 4 | Kaichen Tang | The University of Texas Health… (US) | |
| 5 | Yaobin Ling | The University of Texas Health… (US) | |
| 6 | Muhammad F. Walji | The University of Texas Health… (US) | |
| 7 | Xiaoqian Jiang Corresponding | The University of Texas Health… (US) |
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