Explainable temporal deep learning for athlete-independent classification of injury-labeled days in competitive distance runners: a methodological benchmark using seven-day training-load histories.
- DOI
- 10.3389/fpubh.2026.1936365
- Published
- 2026
- Container
- Frontiers in public health
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.3389/fpubh.2026.1936365,
title = {Explainable temporal deep learning for athlete-independent classification of injury-labeled days in competitive distance runners: a methodological benchmark using seven-day training-load histories.},
author = {Qiao T and Tian W},
year = {2026},
journal = {Frontiers in public health},
doi = {10.3389/fpubh.2026.1936365},
url = {https://doi.org/10.3389/fpubh.2026.1936365}
}RIS
TY - JOUR TI - Explainable temporal deep learning for athlete-independent classification of injury-labeled days in competitive distance runners: a methodological benchmark using seven-day training-load histories. AU - Qiao T AU - Tian W PY - 2026 JO - Frontiers in public health DO - 10.3389/fpubh.2026.1936365 UR - https://doi.org/10.3389/fpubh.2026.1936365 ER -
APA
T, Q., & W, T. (2026). Explainable temporal deep learning for athlete-independent classification of injury-labeled days in competitive distance runners: a methodological benchmark using seven-day training-load histories.. Frontiers in public health. https://doi.org/10.3389/fpubh.2026.1936365
Source records
- pubmed · retrieved 2026-09-25T13:25:31.016Z