Evidence map›Paper›PMID 34531473›Full record

ArticleScientific reports2021

Early outcome detection for COVID-19 patients.

Alina Sîrbu, Greta Barbieri, Francesco Faita, Paolo Ferragina, Luna Gargani, Lorenzo Ghiadoni, Corrado Priami

Abstract read
In one paragraph

Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

7 authors.

Alina SîrbuDepartment of Computer Science, University of Pisa, Pisa, Italy. alina.sirbu@unipi.it.
Greta BarbieriDepartment of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy.
Francesco FaitaInstitute of Clinical Physiology, National Research Council, Pisa, Italy.
Paolo FerraginaDepartment of Computer Science, University of Pisa, Pisa, Italy.
Luna GarganiInstitute of Clinical Physiology, National Research Council, Pisa, Italy.
Lorenzo GhiadoniDepartment of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy.
Corrado PriamiDepartment of Computer Science, University of Pisa, Pisa, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the outbreak of COVID-19 exerting a strong pressure on hospitals and health facilities, clinical decision support systems based on predictive models can help to effectively improve the management of the pandemic. We present a method for predicting mortality for COVID-19 patients. Starting from a large number of clinical variables, we select six of them with largest predictive power, using a feature selection method based on genetic algorithms and starting from a set of COVID-19 patients from the first wave. The algorithm is designed to reduce the impact of missing values in the set of variables measured, and consider only variables that show good accuracy on validation data. The final predictive model provides accuracy larger than 85% on test data, including a new patient cohort from the second COVID-19 wave, and on patients with imputed missing values. The selected clinical variables are confirmed to be relevant by recent literature on COVID-19.

Indexed as

AlgorithmsCohort StudiesCOVID-19Decision Support Systems, ClinicalHumansMachine LearningModels, TheoreticalMortality

Identifiers

PMID34531473
PMCPMC8446000

What OpenQuestion holds

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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.