A New Time-Window Prediction Model For Traumatic Hemorrhagic Shock Based on Interpretable Machine Learning

Early warning prediction of traumatic hemorrhagic shock (THS) can greatly reduce patient mortality and morbidity. We aimed to develop and validate models with different stepped feature sets to predict THS in advance. From the PLA General Hospital Emergency Rescue Database and Medical Information Mar...

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Detalles Bibliográficos
Autores principales: Zhao, Yuzhuo, Jia, Lijing, Jia, Ruiqi, Han, Hui, Feng, Cong, Li, Xueyan, Wei, Zijian, Wang, Hongxin, Zhang, Heng, Pan, Shuxiao, Wang, Jiaming, Guo, Xin, Yu, Zheyuan, Li, Xiucheng, Wang, Zhaohong, Chen, Wei, Li, Jing, Li, Tanshi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Lippincott Williams & Wilkins 2022
Materias:
Clinical Science Aspects
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8663521/
https://www.ncbi.nlm.nih.gov/pubmed/34905530
http://dx.doi.org/10.1097/SHK.0000000000001842

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8663521/
https://www.ncbi.nlm.nih.gov/pubmed/34905530
http://dx.doi.org/10.1097/SHK.0000000000001842

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