Evidence map›Paper›PMID 35888187›Full record

ArticleLife (Basel, Switzerland)2022

Prediction and Classification of COVID-19 Admissions to Intensive Care Units (ICU) Using Weighted Radial Kernel SVM Coupled with Recursive Feature Elimination (RFE).

Huda M Alshanbari, Tahir Mehmood, Waqas Sami, Wael Alturaiki, Mauawia A Hamza, Bandar Alosaimi

Open access · goldAbstract read
In one paragraph

Article in Life (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
2.0field-weighted citation impact, top 14% of its field
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

5 citing papers in PubMed, 14 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Virology Applications to the COVID-19 Pandemic.Life (Basel, Switzerland) · 2025
    Article
  5. 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

6 authors at 4 institutions in 2 countries.

Huda M AlshanbariDepartment of Mathematical Sciences, College of Science, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.ORCID 0000-0001-5154-7477
Tahir MehmoodSchool of Natural Sciences (SNS), National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan.ORCID 0000-0001-9775-8093
Waqas SamiDepartment of Community Medicine and Public Health, College of Medicine, Majmaah University, Majmaah 11952, Saudi Arabia.ORCID 0000-0001-7948-1190
Wael AlturaikiDepartment of Medical Laboratory Sciences, College of Applied Medical Sciences, Majmaah University, Majmaah 11952, Saudi Arabia.ORCID 0000-0002-6947-7582
Mauawia A HamzaFaculty of Medicine, King Fahad Medical City, Riyadh 11525, Saudi Arabia.
Bandar AlosaimiResearch Center, King Fahad Medical City, Riyadh 11525, Saudi Arabia.ORCID 0000-0003-4719-2655
King Fahd Medical City · SAMajmaah University · SANational University of Sciences and Technology · PKPrincess Nourah bint Abdulrahman University · SA

Funding

Princess Nourah bint Abdulrahman University PNURSP2022R299
6 · The paper itself

Abstract

Healthcare systems have been under immense pressure since the beginning of the COVID-19 pandemic; hence, studies on using machine learning (ML) methods for classifying ICU admissions and resource allocation are urgently needed. We investigated whether ML can propose a useful classification model for predicting the ICU admissions of COVID-19 patients. In this retrospective study, the clinical characteristics and laboratory findings of 100 patients with laboratory-confirmed COVID-19 tests were retrieved between May 2020 and January 2021. Based on patients' demographic and clinical data, we analyzed the capability of the proposed weighted radial kernel support vector machine (SVM), coupled with

Indexed as

classificationCOVID-19 burdenhealthcare systemsICUmachine learningpredictionpublic health measuressupport vector machine

Identifiers

PMID35888187
PMCPMC9318483
OpenAlexW4286434162

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.