Title: Quantitative prognostic factor extraction of epidemic thrombosis using machine learning strategy
Authors: Tianle Zhou; Danni Deng; Chaoyi Chu; Jie Cao
Addresses: Jiangsu University of Technology, Changzhou, Jiangsu, China ' Department of Neurosurgery, The First People's Hospital of Changzhou, Changzhou, Jiangsu, China ' Jiangsu University of Technology, Changzhou, Jiangsu, China ' Department of Neurosurgery, The First People's Hospital of Changzhou, Changzhou, Jiangsu, China
Abstract: In recent years, artificial intelligence and machine learning have become increasingly involved in the treatment of prevalent human diseases. Acute ischemic stroke (AIS) is an increasingly severe disease with a high risk of thrombosis resulting in loss of neurological function or death. MT with mechanical thrombectomy has become the mainstream treatment. Apart from the common factors such as blood glucose, NIHSS, and blood pressure level, etc., there are still unknown factors which may have influence for prognosis after MT surgery. In this study, with the help of machine learning strategy, high-dimensional data of patients are mined, and the AIS prognostic prediction model is established in order to quantify the key influencing factors and determine the relationship between these parameters and the prognosis. This study is supposed to provide a set of methodology to evaluate the prognosis effectively.
Keywords: acute ischemic stroke; AIS; mechanical thrombectomy; prognosis factor extraction; high-dimensional data; machine learning.
International Journal of Hybrid Intelligence, 2021 Vol.2 No.1, pp.79 - 89
Received: 03 Feb 2019
Accepted: 09 Apr 2019
Published online: 26 Sep 2021 *