Evidence map›Paper›PMID 36290506›Full record

ArticleBioengineering (Basel, Switzerland)2022

Predicting the Level of Respiratory Support in COVID-19 Patients Using Machine Learning.

Hisham Abdeltawab, Fahmi Khalifa, Yaser ElNakieb, Ahmed Elnakib, Fatma Taher, Norah Saleh Alghamdi, Harpal Singh Sandhu, Ayman El-Baz

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Bioengineering (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Machine Learning for Biomedical Applications.Bioengineering (Basel, Switzerland) · 2024
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Hisham AbdeltawabDepartment of Bioengineering, University of Louisville, Louisville, KY 40292, USA.ORCID 0000-0001-6268-5955
Fahmi KhalifaDepartment of Bioengineering, University of Louisville, Louisville, KY 40292, USA.ORCID 0000-0003-3318-2851
Yaser ElNakiebDepartment of Bioengineering, University of Louisville, Louisville, KY 40292, USA.ORCID 0000-0002-0313-136X
Ahmed ElnakibDepartment of Bioengineering, University of Louisville, Louisville, KY 40292, USA.ORCID 0000-0001-6084-3622
Fatma TaherCollege of Technological Innovation, Zayed University, Dubai P.O. Box 19282, United Arab Emirates.ORCID 0000-0001-8358-9081
Norah Saleh AlghamdiDepartment of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.ORCID 0000-0001-6421-6001
Harpal Singh SandhuDepartment of Bioengineering, University of Louisville, Louisville, KY 40292, USA.
Ayman El-BazDepartment of Bioengineering, University of Louisville, Louisville, KY 40292, USA.ORCID 0000-0001-7264-1323

Funding

Princess Nourah bint Abdulrahman University Researchers Supporting Project, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia. PNURSP2022R40
6 · The paper itself

Abstract

In this paper, a machine learning-based system for the prediction of the required level of respiratory support in COVID-19 patients is proposed. The level of respiratory support is divided into three classes: class 0 which refers to minimal support, class 1 which refers to non-invasive support, and class 2 which refers to invasive support. A two-stage classification system is built. First, the classification between class 0 and others is performed. Then, the classification between class 1 and class 2 is performed. The system is built using a dataset collected retrospectively from 3491 patients admitted to tertiary care hospitals at the University of Louisville Medical Center. The use of the feature selection method based on analysis of variance is demonstrated in the paper. Furthermore, a dimensionality reduction method called principal component analysis is used. XGBoost classifier achieves the best classification accuracy (84%) in the first stage. It also achieved optimal performance in the second stage, with a classification accuracy of 83%.

Indexed as

COVID-19feature selectionmachine learningrespiratory support

Identifiers

PMID36290506
PMCPMC9598090

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.