HIGHLIGHTS
- who: Anass Bouchnita et al. from the Department of Mathematical Sciences, The University of Texas at El Paso, El Paso, TX, USA have published the paper: Combining Computational Modelling and Machine Learning to Identify COVID-19 Patients with a High Thromboembolism Risk, in the Journal: Mathematics 2023, 11, 289. of 15/Nov/2022
- what: This work proposes a methodology that identifies COVID-19 patients with a high thromboembolism risk using computational modelling and machine learning. Next the authors show that COVID-19 upregulates the peak concentration of thrombin generation (TG) and its endogenous thrombin potential . . .
If you want to have access to all the content you need to log in!
Thanks :)
If you don't have an account, you can create one here.