Detection of pulmonary embolism severity using clinical characteristics, hematological indices, and machine learning techniques

Detection of pulmonary embolism severity using clinical characteristics, hematological indices, and machine learning techniques

IntroductionPulmonary embolism (PE) is a cardiopulmonary condition that can be fatal. PE can lead to sudden cardiovascular collapse and is potentially life-threatening, necessitating risk classification to modify therapy following the diagnosis of PE. We collected clinical characteristics, routine b...

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Bibliographic Details
Main Authors: Hang Su, Zhengyuan Han, Yujie Fu, Dong Zhao, Fanhua Yu, Ali Asghar Heidari, Yu Zhang, Yeqi Shou, Peiliang Wu, Huiling Chen, Yanfan Chen
Format: Article
Language:English
Published: Frontiers Media S.A. 2022-12-01
Series:Frontiers in Neuroinformatics
Subjects:
pulmonary embolism
feature selection
extreme learning machine
disease diagnosis
machine learning
meta-heuristic
Online Access:https://www.frontiersin.org/articles/10.3389/fninf.2022.1029690/full

Internet

https://www.frontiersin.org/articles/10.3389/fninf.2022.1029690/full

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