Predicting prolonged length of stay after day-case urolithiasis surgery: a machine learning approach
- DOI
- 10.1186/s12911-026-03755-z
- Published
- 2026-08-10
- Container
- BMC Medical Informatics and Decision Making
- Publisher
- Springer Science and Business Media LLC
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1186/s12911-026-03755-z,
title = {Predicting prolonged length of stay after day-case urolithiasis surgery: a machine learning approach},
author = {Chunxiang Liu and Guolong Zhang and Yehan Feng and Fang Wang and Yiling Chen and Yongda Liu and Hongling Sun},
year = {2026},
journal = {BMC Medical Informatics and Decision Making},
doi = {10.1186/s12911-026-03755-z},
url = {https://doi.org/10.1186/s12911-026-03755-z}
}RIS
TY - JOUR TI - Predicting prolonged length of stay after day-case urolithiasis surgery: a machine learning approach AU - Chunxiang Liu AU - Guolong Zhang AU - Yehan Feng AU - Fang Wang AU - Yiling Chen AU - Yongda Liu AU - Hongling Sun PY - 2026 JO - BMC Medical Informatics and Decision Making DO - 10.1186/s12911-026-03755-z UR - https://doi.org/10.1186/s12911-026-03755-z ER -
APA
Liu, C., Zhang, G., Feng, Y., Wang, F., Chen, Y., Liu, Y., & Sun, H. (2026). Predicting prolonged length of stay after day-case urolithiasis surgery: a machine learning approach. BMC Medical Informatics and Decision Making. https://doi.org/10.1186/s12911-026-03755-z
Source records
- crossref · retrieved 2026-11-07T06:24:28.542Z