ArticleJournal of evaluation in clinical practice2020
Fuzzy logic-based clinical decision support system for the evaluation of renal function in post-Transplant Patients.
Article in Journal of evaluation in clinical practice, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- From assistant to collaborator: A systematic review of the evolution of artificial intelligence in end-stage renal disease care and management.PLOS digital health · 2026Article
- Development and evaluation of a clinical dashboard for nephrology inpatients: a pilot study.BMC medical informatics and decision making · 2026Article
- Early Implications for Solid Organ Transplantation With the Use of Artificial Intelligence From a Bibliometric Perspective.Mayo Clinic proceedings. Digital health · 2026Review
- ISPAD, bridging clinical expertise and AI for autoimmune-related pernicious anemia diagnosis.Frontiers in immunology · 2026Article
- Fuzzy logic nursing tool for early acute kidney injury detection in surgical patients.Frontiers in nephrology · 2025Article
- Present and Future Applications of Artificial Intelligence in Kidney Transplantation.Journal of clinical medicine · 2024Review
- Classification and regression model to manage the hospitalization for laparoscopic cholecystectomy.Scientific reports · 2023Article
- Synthesizing Electronic Health Records for Predictive Models in Low-Middle-Income Countries (LMICs).Biomedicines · 2023Article
- Comparative Study of Fuzzy Rule-Based Classifiers for Medical Applications.Sensors (Basel, Switzerland) · 2023Article
- Lean Management Approach for Reengineering the Hospital Cardiology Consultation Process: A Report from AORN "A. Cardarelli" of Naples.International journal of environmental research and public health · 2022Article
- A Fuzzy Inference System for the Assessment of Indoor Air Quality in an Operating Room to Prevent Surgical Site Infection.International journal of environmental research and public health · 2022Article
- A Health Technology Assessment in Maxillofacial Cancer Surgery by Using the Six Sigma Methodology.International journal of environmental research and public health · 2021Article
- Healthcare Digitalisation and the Changing Nature of Work and Society.Healthcare (Basel, Switzerland) · 2021Article
- Comparative Evaluation of the Treatment of COVID-19 with Multicriteria Decision-Making Techniques.Journal of healthcare engineering · 2021Article
- Fuzzy logic-based clinical decision support system for the evaluation of renal function in post-Transplant Patients.Journal of evaluation in clinical practice · 2020Article
- Promises of Big Data and Artificial Intelligence in Nephrology and Transplantation.Journal of clinical medicine · 2020Article
- Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesIn 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.
methodBased 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).
resultsThe 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.
conclusionsWe propose that the CDSSs for the assessment and follow-up of kidney-transplanted patients built in this study are applicable to clinical practice.
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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 Socratic graph.