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Is It Possible to Analyze Kidney Functions, Electrolytes and Volemia Using Artificial Intelligence?

Markers used in everyday clinical practice cannot distinguish between the permanent impairment of renal function. Sodium and potassium values and their interdependence are key parameters in addition to volemia for the assessment of cardiorenal balance. The aim of this study was to investigate volemi...

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Autores principales: Tasić, Danijela, Đorđević, Katarina, Galović, Slobodanka, Furundžić, Draško, Dimitrijević, Zorica, Radenković, Sonja
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9777538/
https://www.ncbi.nlm.nih.gov/pubmed/36553138
http://dx.doi.org/10.3390/diagnostics12123131
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author Tasić, Danijela
Đorđević, Katarina
Galović, Slobodanka
Furundžić, Draško
Dimitrijević, Zorica
Radenković, Sonja
author_facet Tasić, Danijela
Đorđević, Katarina
Galović, Slobodanka
Furundžić, Draško
Dimitrijević, Zorica
Radenković, Sonja
author_sort Tasić, Danijela
collection PubMed
description Markers used in everyday clinical practice cannot distinguish between the permanent impairment of renal function. Sodium and potassium values and their interdependence are key parameters in addition to volemia for the assessment of cardiorenal balance. The aim of this study was to investigate volemia and electrolyte status from a clinical cardiorenal viewpoint under consideration of renal function utilizing artificial intelligence. In this paper, an analysis of five variables: B-type natriuretic peptide, sodium, potassium, ejection fraction, EPI creatinine-cystatin C, was performed using an algorithm based on the adaptive neuro fuzzy inference system. B-type natriuretic peptide had the greatest influence on the ejection fraction. It has been shown that values of both Na+ and K+ lead to deterioration of the condition and vital endangerment of patients. To identify the risk of occurrence, the model identifies a prognostic biomarker by random regression from the total data set. The predictions obtained from this model can help optimize preventative strategies and intensive monitoring for patients identified as at risk for electrolyte disturbance and hypervolemia. This approach may be superior to the traditional diagnostic approach due to its contribution to more accurate and rapid diagnostic interpretation and better planning of further patient treatment
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spelling pubmed-97775382022-12-23 Is It Possible to Analyze Kidney Functions, Electrolytes and Volemia Using Artificial Intelligence? Tasić, Danijela Đorđević, Katarina Galović, Slobodanka Furundžić, Draško Dimitrijević, Zorica Radenković, Sonja Diagnostics (Basel) Article Markers used in everyday clinical practice cannot distinguish between the permanent impairment of renal function. Sodium and potassium values and their interdependence are key parameters in addition to volemia for the assessment of cardiorenal balance. The aim of this study was to investigate volemia and electrolyte status from a clinical cardiorenal viewpoint under consideration of renal function utilizing artificial intelligence. In this paper, an analysis of five variables: B-type natriuretic peptide, sodium, potassium, ejection fraction, EPI creatinine-cystatin C, was performed using an algorithm based on the adaptive neuro fuzzy inference system. B-type natriuretic peptide had the greatest influence on the ejection fraction. It has been shown that values of both Na+ and K+ lead to deterioration of the condition and vital endangerment of patients. To identify the risk of occurrence, the model identifies a prognostic biomarker by random regression from the total data set. The predictions obtained from this model can help optimize preventative strategies and intensive monitoring for patients identified as at risk for electrolyte disturbance and hypervolemia. This approach may be superior to the traditional diagnostic approach due to its contribution to more accurate and rapid diagnostic interpretation and better planning of further patient treatment MDPI 2022-12-12 /pmc/articles/PMC9777538/ /pubmed/36553138 http://dx.doi.org/10.3390/diagnostics12123131 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Tasić, Danijela
Đorđević, Katarina
Galović, Slobodanka
Furundžić, Draško
Dimitrijević, Zorica
Radenković, Sonja
Is It Possible to Analyze Kidney Functions, Electrolytes and Volemia Using Artificial Intelligence?
title Is It Possible to Analyze Kidney Functions, Electrolytes and Volemia Using Artificial Intelligence?
title_full Is It Possible to Analyze Kidney Functions, Electrolytes and Volemia Using Artificial Intelligence?
title_fullStr Is It Possible to Analyze Kidney Functions, Electrolytes and Volemia Using Artificial Intelligence?
title_full_unstemmed Is It Possible to Analyze Kidney Functions, Electrolytes and Volemia Using Artificial Intelligence?
title_short Is It Possible to Analyze Kidney Functions, Electrolytes and Volemia Using Artificial Intelligence?
title_sort is it possible to analyze kidney functions, electrolytes and volemia using artificial intelligence?
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9777538/
https://www.ncbi.nlm.nih.gov/pubmed/36553138
http://dx.doi.org/10.3390/diagnostics12123131
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