Explainable Machine-Learning Models for COVID-19 Prognosis Prediction Using Clinical, Laboratory and Radiomic Features-Reference-Cited by-同舟云学术

Explainable Machine-Learning Models for COVID-19 Prognosis Prediction Using Clinical, Laboratory and Radiomic Features

Published:2023 Issue: Volume:11 Page:121492-121510
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Prinzi Francesco¹^ORCID,Militello Carmelo²^ORCID,Scichilone Nicola³^ORCID,Gaglio Salvatore²^ORCID,Vitabile Salvatore¹^ORCID

Affiliation:

1. Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Palermo, Italy

2. Institute for High-Performance Computing and Networking (ICAR-CNR), Italian National Research Council, Palermo, Italy

3. Department of Health Promotion Sciences, Maternal and Infant Care, Internal Medicine and Medical Specialties (PROMISE), Division of Respiratory Diseases, University of Palermo, Palermo, Italy

Funder

European Union – NextGenerationEU – MUR D.M.

“A Trustworthy Clinical Decision Support System for non-communicable diseases detection and prediction.”

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10296889.pdf?arnumber=10296889

Reference72 articles.

1. Texture analysis using gray level run lengths

2. AIforCOVID: Predicting the clinical outcomes in patients with COVID-19 applying AI to chest-X-rays. An Italian multicentre study

3. BS-Net: Learning COVID-19 pneumonia severity on a large chest X-ray dataset

4. Radiomics: Images Are More than Pictures, They Are Data

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