Current laboratory prediction systems in nephrology face challenges such as handling non-stationary datasets, limited accuracy, and insufficient personalization. To address these issues, this study introduces three ma...
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Current laboratory prediction systems in nephrology face challenges such as handling non-stationary datasets, limited accuracy, and insufficient personalization. To address these issues, this study introduces three machine learning-based models: the Adaptive Predictive Model for Laboratory Results with Patient-specific Adaptation (APMLR), the Adaptive Input-Output Model for eGFR Prediction based on Other Results (AIOM), and the Intelligent Assessment Model for Renal Function (IAMRF). These models leverage advanced algorithms to improve the accuracy and reliability of predictions for critical parameters such as eGFR, creatinine, and urea levels. The APMLR system achieved superior performance with Linear SVR, reaching a prediction accuracy of up to 96.97%, while Gradient Boosting emerged as the most effective method for both AIOM and IAMRF systems (approx. 95%). These findings highlight the potential of machine learning to enhance nephrology patient care by automating diagnoses, improving operational workflows, and setting anew standard for renal function assessment in clinical practice.
This study explores the contemporary landscape of integrating numerical algorithms, artificial intelligence (AI), and expert methodologies within the domain of nephrology. Focusing on automation and decision support, ...
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This study explores the contemporary landscape of integrating numerical algorithms, artificial intelligence (AI), and expert methodologies within the domain of nephrology. Focusing on automation and decision support, we scrutinize the impact of numerical algorithms on the precise evaluation of kidney function. Furthermore, we delve into the transformative potential of artificial intelligence, particularly in the realms of machine learning and deep learning applications, elucidating its role in early disease detection and the formulation of personalized treatment strategies. The synergy between computer-based tools and expert-driven approaches is examined, underscoring their collaborative role in enhancing the accuracy and dependability of diagnoses. Additionally, we address the ethical considerations and challenges associated with the incorporation of automation in nephrology. The paper provides a concise overview of in-depth analysis while illustrating the promising prospects of these innovative methodologies for reshaping nephrology research and patient care.
The diagnosis and management of idiopathic membranous nephropathy (IMN) is a complex clinical challenge due to the disease’s unpredictable progression and the varying responses to treatment. Traditional methods of ri...
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The diagnosis and management of idiopathic membranous nephropathy (IMN) is a complex clinical challenge due to the disease’s unpredictable progression and the varying responses to treatment. Traditional methods of risk stratification and treatment planning often rely on manual assessments, which can lead to inconsistent decision-making and suboptimal patient outcomes. To address this issue, we propose an expert system that leverages machine learning (ML) and artificial intelligence (AI) models and a knowledge-based approach to automate risk classification and treatment recommendations for IMN patients. This system aims to standardize and automate clinical decision-making, improve diagnostic accuracy, and enhance patient care through data-driven insights.
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