Automated prediction of COVID-19 mortality outcome using clinical and laboratory data based on hierarchical feature selection and random forest classifier-Reference-Cited by-同舟云学术

Automated prediction of COVID-19 mortality outcome using clinical and laboratory data based on hierarchical feature selection and random forest classifier

Published:2022-03-17 Issue:2 Volume:26 Page:160-173
ISSN:1025-5842
Container-title:Computer Methods in Biomechanics and Biomedical Engineering
language:en
Short-container-title:Computer Methods in Biomechanics and Biomedical Engineering

Author:

Amini Nasrin¹,Mahdavi Mahdi²³,Choubdar Hadi²³,Abedini Atefeh⁴,Shalbaf Ahmad¹,Lashgari Reza²

Affiliation:

1. Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran

2. Institute of Medical Science and Technology (IMSAT), Shahid Beheshti University, Tehran, Iran

3. School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran

4. Chronic Respiratory Diseases Research Center, National Research Institute of Tuberculosis and Lung Diseases (NRITLD), Shahid Beheshti University of Medical Sciences, Tehran, Iran

Funder

Iran National Science Foundation: INSF

Research Department of School of Medicine Shahid Beheshti University of Medical Sciences

Publisher

Informa UK Limited

Subject

Computer Science Applications,Human-Computer Interaction,Biomedical Engineering,General Medicine,Bioengineering

Link

https://www.tandfonline.com/doi/pdf/10.1080/10255842.2022.2050906

Reference61 articles.

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2. Predicting COVID-19 Malignant Progression with AI Techniques

3. Batllés PC, Cerdá-Alberich L, Fonfría-Esparcia C, Carreres-Ortega A, Muñoz-Núñez CF, Trilles-Olaso L, Martí-Bonmatí L. 2022. Development of severity and mortality prediction models for COVID-19 patients at emergency department including the chest X-ray. Radiología (English Edition).

4. A survey on feature selection methods

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