Interpretable machine learning for self-reported reduced uncorrected visual acuity in school students.
- DOI
- 10.3389/fpubh.2026.1872000
- Published
- 2026
- Container
- Frontiers in public health
- Publisher
- Not recorded
- Open access
- yes
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limited evidence Score 45/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.
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Cite this work
BibTeX
@article{allodium:10.3389/fpubh.2026.1872000,
title = {Interpretable machine learning for self-reported reduced uncorrected visual acuity in school students.},
author = {Li Y and Zhang L and Wang J and Fu S and Liu Y and Yuan L and Ye M},
year = {2026},
journal = {Frontiers in public health},
doi = {10.3389/fpubh.2026.1872000},
url = {https://doi.org/10.3389/fpubh.2026.1872000}
}RIS
TY - JOUR TI - Interpretable machine learning for self-reported reduced uncorrected visual acuity in school students. AU - Li Y AU - Zhang L AU - Wang J AU - Fu S AU - Liu Y AU - Yuan L AU - Ye M PY - 2026 JO - Frontiers in public health DO - 10.3389/fpubh.2026.1872000 UR - https://doi.org/10.3389/fpubh.2026.1872000 ER -
APA
Y, L., L, Z., J, W., S, F., Y, L., L, Y., & M, Y. (2026). Interpretable machine learning for self-reported reduced uncorrected visual acuity in school students.. Frontiers in public health. https://doi.org/10.3389/fpubh.2026.1872000
Source records
- pubmed · retrieved 2026-11-10T19:11:18.926Z