Development and validation of an automatic machine learning model to predict abnormal increase of transaminase in valproic acid-treated epilepsy-Reference-Cited by-同舟云学术

Development and validation of an automatic machine learning model to predict abnormal increase of transaminase in valproic acid-treated epilepsy

Published:2024-06-16 Issue:9 Volume:98 Page:3049-3061
ISSN:0340-5761
Container-title:Archives of Toxicology
language:en
Short-container-title:Arch Toxicol

Author:

Ma Hongying,Huang Sihui,Li Fengxin,Pang Zicheng,Luo Jian,Sun Danfeng,Liu Junsong,Chen Zhuoming,Qu Jian,Qu Qiang^ORCID

Funder

National Natural Science Foundation of China

Health Commission of Hunan Province

Natural Science Foundation of Hunan Province

Natural Science Foundation of Changsha

Degree & Postgraduate Education Reform Project of Central South University

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s00204-024-03803-5.pdf

Reference38 articles.

1. Andrade RJ, Chalasani N, Bjornsson ES, et al. (2019) Drug-induced liver injury. Nat Rev Dis Primers 5(1):58. https://doi.org/10.1038/s41572-019-0105-0

2. Belfield SJ, Cronin MTD, Enoch SJ, Firman JW (2023) Guidance for good practice in the application of machine learning in development of toxicological quantitative structure-activity relationships (QSARs). PloS one 18(5):e0282924. https://doi.org/10.1371/journal.pone.0282924

3. Buchanan J, Li M, Ni X, Wildfire J (2021) A new paradigm for safety data signal detection and evaluation using open-source software created by an interdisciplinary working group. Therap Innov Regul Sci 55(6):1214–1219. https://doi.org/10.1007/s43441-021-00319-3

4. Cerrada M, Trujillo L, Hernández DE et al (2022) AutoML for feature selection and model tuning applied to fault severity diagnosis in spur gearboxes. Math Comput Appl 27(1):6

5. Chu H, Zhang X, Shi J, Zhou Z, Yang X (2023) Antiseizure medications for idiopathic generalized epilepsies: a systematic review and network meta-analysis. J Neurol 270(10):4713–4728. https://doi.org/10.1007/s00415-023-11834-8