• Giovanni Improta, Valeria Mazzella, Donatella Vecchione, Stefania Santini, and Maria Triassi.
• Department of Public Health of the University Hospital, University of Naples Federico II, Naples, Italy.
• J Eval Clin Pract. 2020 Aug 1; 26 (4): 122412341224-1234.

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.© 2019 The Authors. Journal of Evaluation in Clinical Practice published by John Wiley & Sons Ltd.

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