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Fuzzy logic–based clinical decision support system for the evaluation of renal function in post‐Transplant Patients
OBJECTIVES: In the context of the gradual development of artificial intelligence in health care, the clinical decision support systems (CDSS) play an increasing crucial role in improving the quality of the therapeutic and diagnostic efficiency in health care. The fuzzy logic (FL) provides an effecti...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7496862/ https://www.ncbi.nlm.nih.gov/pubmed/31713997 http://dx.doi.org/10.1111/jep.13302 |
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author | Improta, Giovanni Mazzella, Valeria Vecchione, Donatella Santini, Stefania Triassi, Maria |
author_facet | Improta, Giovanni Mazzella, Valeria Vecchione, Donatella Santini, Stefania Triassi, Maria |
author_sort | Improta, Giovanni |
collection | PubMed |
description | OBJECTIVES: In the context of the gradual development of artificial intelligence in health care, the clinical decision support systems (CDSS) play an increasing crucial role in improving the quality of the therapeutic and diagnostic efficiency in health care. The fuzzy logic (FL) provides an effective means for dealing with uncertainties in the health decision‐making process; therefore, FL‐based CDSS becomes a very powerful tool for data and knowledge management, being able to think like an expert clinician. This work proposes an FL‐based CDSS for the evaluation of renal function in posttransplant patients. METHOD: Based on the data provided by the Department of Nephrology of the University Hospital Federico II of Naples, a statistical sample is selected according to appropriate inclusion criteria. Four fuzzy inference systems are implemented monitoring the renal function by the level of proteinuria and the glomerular filtration rate (GFR). RESULTS: The systems show an accuracy of more than 90% and the outputs are provided through easy to read graphics, so that physicians can intuitively monitor the patient's clinical status, with the objective to improve drugs dosage and reduce medication errors. CONCLUSIONS: We propose that the CDSSs for the assessment and follow‐up of kidney‐transplanted patients built in this study are applicable to clinical practice. |
format | Online Article Text |
id | pubmed-7496862 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-74968622020-09-25 Fuzzy logic–based clinical decision support system for the evaluation of renal function in post‐Transplant Patients Improta, Giovanni Mazzella, Valeria Vecchione, Donatella Santini, Stefania Triassi, Maria J Eval Clin Pract Original Papers OBJECTIVES: In the context of the gradual development of artificial intelligence in health care, the clinical decision support systems (CDSS) play an increasing crucial role in improving the quality of the therapeutic and diagnostic efficiency in health care. The fuzzy logic (FL) provides an effective means for dealing with uncertainties in the health decision‐making process; therefore, FL‐based CDSS becomes a very powerful tool for data and knowledge management, being able to think like an expert clinician. This work proposes an FL‐based CDSS for the evaluation of renal function in posttransplant patients. METHOD: Based on the data provided by the Department of Nephrology of the University Hospital Federico II of Naples, a statistical sample is selected according to appropriate inclusion criteria. Four fuzzy inference systems are implemented monitoring the renal function by the level of proteinuria and the glomerular filtration rate (GFR). RESULTS: The systems show an accuracy of more than 90% and the outputs are provided through easy to read graphics, so that physicians can intuitively monitor the patient's clinical status, with the objective to improve drugs dosage and reduce medication errors. CONCLUSIONS: We propose that the CDSSs for the assessment and follow‐up of kidney‐transplanted patients built in this study are applicable to clinical practice. John Wiley and Sons Inc. 2019-11-12 2020-08 /pmc/articles/PMC7496862/ /pubmed/31713997 http://dx.doi.org/10.1111/jep.13302 Text en © 2019 The Authors. Journal of Evaluation in Clinical Practice published by John Wiley & Sons Ltd This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Papers Improta, Giovanni Mazzella, Valeria Vecchione, Donatella Santini, Stefania Triassi, Maria Fuzzy logic–based clinical decision support system for the evaluation of renal function in post‐Transplant Patients |
title | Fuzzy logic–based clinical decision support system for the evaluation of renal function in post‐Transplant Patients |
title_full | Fuzzy logic–based clinical decision support system for the evaluation of renal function in post‐Transplant Patients |
title_fullStr | Fuzzy logic–based clinical decision support system for the evaluation of renal function in post‐Transplant Patients |
title_full_unstemmed | Fuzzy logic–based clinical decision support system for the evaluation of renal function in post‐Transplant Patients |
title_short | Fuzzy logic–based clinical decision support system for the evaluation of renal function in post‐Transplant Patients |
title_sort | fuzzy logic–based clinical decision support system for the evaluation of renal function in post‐transplant patients |
topic | Original Papers |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7496862/ https://www.ncbi.nlm.nih.gov/pubmed/31713997 http://dx.doi.org/10.1111/jep.13302 |
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