Early Detection of Mortality Risk in Preterm Neonates Using a Random Forest Classifier and Tree-Based Pipeline Optimization Tool Automated Machine Learning
- DOI
- 10.1097/fm9.0000000000000366
- Published
- 2026-06-18
- Container
- Maternal-Fetal Medicine
- Publisher
- Ovid Technologies (Wolters Kluwer Health)
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1097/fm9.0000000000000366,
title = {Early Detection of Mortality Risk in Preterm Neonates Using a Random Forest Classifier and Tree-Based Pipeline Optimization Tool Automated Machine Learning},
author = {Vahid Monfared and Ladan Haghighi and Afkham Hashemi and Neda Farzizadeh and Zahra Najmi},
year = {2026},
journal = {Maternal-Fetal Medicine},
doi = {10.1097/fm9.0000000000000366},
url = {https://doi.org/10.1097/fm9.0000000000000366}
}RIS
TY - JOUR TI - Early Detection of Mortality Risk in Preterm Neonates Using a Random Forest Classifier and Tree-Based Pipeline Optimization Tool Automated Machine Learning AU - Vahid Monfared AU - Ladan Haghighi AU - Afkham Hashemi AU - Neda Farzizadeh AU - Zahra Najmi PY - 2026 JO - Maternal-Fetal Medicine DO - 10.1097/fm9.0000000000000366 UR - https://doi.org/10.1097/fm9.0000000000000366 ER -
APA
Monfared, V., Haghighi, L., Hashemi, A., Farzizadeh, N., & Najmi, Z. (2026). Early Detection of Mortality Risk in Preterm Neonates Using a Random Forest Classifier and Tree-Based Pipeline Optimization Tool Automated Machine Learning. Maternal-Fetal Medicine. https://doi.org/10.1097/fm9.0000000000000366
Source records
- crossref · retrieved 2026-11-07T22:32:40.579Z