Machine learning-based prediction of the post-thrombotic syndrome: Model development and validation study

BACKGROUND: Prevention is highly involved in reducing the incidence of post-thrombotic syndrome (PTS). We aimed to develop accurate models with machine learning (ML) algorithms to predict whether PTS would occur within 24 months. MATERIALS AND METHODS: The clinical data used for model building were...

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Detalles Bibliográficos
Autores principales: Yu, Tao, Shen, Runnan, You, Guochang, Lv, Lin, Kang, Shimao, Wang, Xiaoyan, Xu, Jiatang, Zhu, Dongxi, Xia, Zuqi, Zheng, Junmeng, Huang, Kai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Cardiovascular Medicine
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9523080/
https://www.ncbi.nlm.nih.gov/pubmed/36186967
http://dx.doi.org/10.3389/fcvm.2022.990788

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9523080/
https://www.ncbi.nlm.nih.gov/pubmed/36186967
http://dx.doi.org/10.3389/fcvm.2022.990788

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