An optimized machine learning model based on hematological indicators for the noninvasive identification of baicalin’s therapeutic effects in pulmonary hypertension
- DOI
- 10.1080/10255842.2026.2634406
- Published
- 2026-03-03
- Container
- Computer Methods in Biomechanics and Biomedical Engineering
- Publisher
- Informa UK Limited
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1080/10255842.2026.2634406,
title = {An optimized machine learning model based on hematological indicators for the noninvasive identification of baicalin’s therapeutic effects in pulmonary hypertension},
author = {Dong Zhao and Yupeng Li and Yang Yang and Fanhua Yu and Ali Asghar Heidari and Yi Chen and Huiling Chen and Peiliang Wu},
year = {2026},
journal = {Computer Methods in Biomechanics and Biomedical Engineering},
doi = {10.1080/10255842.2026.2634406},
url = {https://doi.org/10.1080/10255842.2026.2634406}
}RIS
TY - JOUR TI - An optimized machine learning model based on hematological indicators for the noninvasive identification of baicalin’s therapeutic effects in pulmonary hypertension AU - Dong Zhao AU - Yupeng Li AU - Yang Yang AU - Fanhua Yu AU - Ali Asghar Heidari AU - Yi Chen AU - Huiling Chen AU - Peiliang Wu PY - 2026 JO - Computer Methods in Biomechanics and Biomedical Engineering DO - 10.1080/10255842.2026.2634406 UR - https://doi.org/10.1080/10255842.2026.2634406 ER -
APA
Zhao, D., Li, Y., Yang, Y., Yu, F., Heidari, A. A., Chen, Y., Chen, H., & Wu, P. (2026). An optimized machine learning model based on hematological indicators for the noninvasive identification of baicalin’s therapeutic effects in pulmonary hypertension. Computer Methods in Biomechanics and Biomedical Engineering. https://doi.org/10.1080/10255842.2026.2634406
Source records
- crossref · retrieved 2026-09-26T10:15:54.786Z