Data Analysis of Impaired Renal and Cardiac Function Using a Combination of Standard Classifiers.

Danijela Tasic, Drasko Furundzic, Katarina Djordjevic, Slobodanka Galovic, Zorica Dimitrijevic, Sonja Radenkovic

Abstract read

In one paragraph

Article in Journal of personalized medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Danijela TasicClinic of Nephrology, UCC Nis, Medical Faculty, University of Nis, 18000 Nis, Serbia.ORCID 0000-0003-3357-4791

Drasko FurundzicInstitute "Mihajlo Pupin", University of Belgrade, 11060 Belgrade, Serbia.

Katarina DjordjevicVinca Institute of Nuclear Science-National Institute of the Republic of Serbia, University of Belgrade, 11000 Belgrade, Serbia.ORCID 0000-0002-1397-8011

Slobodanka GalovicVinca Institute of Nuclear Science-National Institute of the Republic of Serbia, University of Belgrade, 11000 Belgrade, Serbia.ORCID 0000-0002-6728-8868

Zorica DimitrijevicClinic of Nephrology, UCC Nis, Medical Faculty, University of Nis, 18000 Nis, Serbia.ORCID 0000-0002-4944-1822

Sonja RadenkovicClinic of Nephrology, UCC Nis, Medical Faculty, University of Nis, 18000 Nis, Serbia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We examine the significance of the predictive potential of EPI cystatin C (EPI CysC) in combination with NTproBNP, sodium, and potassium in the evaluation of renal function in patients with cardiorenal syndrome using standard mathematical classification models from the domain of artificial intelligence. The criterion for the inclusion of subjects with combined impairment of heart and kidney function in the study was the presence of newly discovered or previously diagnosed clinically manifest cardiovascular disease and acute or chronic kidney disease in different stages of evolution. In this paper, five standard classifiers from the field of machine learning were used for the analysis of the obtained data: ensemble of neural networks (MLP), ensemble of

Indexed as

forecasting ensemblesheartkidneyk-nearest neighbormachine learningmarkersnaive Bayes classifierneural networks

Identifiers

PMID36983618

DOI10.3390/jpm13030437

PMCPMC10058317

What OpenQuestion holds

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LicenceCC BY

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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.