Thermodynamic and Entropic Imbalances in Glomerular Filtration: Modeling Ionic Disorder through Mass Transfer Disruption Using Multiple Artificial Intelligence and Biostatistical Algorithms
Year: 2026
Type: article
Study design: Observational Study
Abstract: Unlike classical Starling hydrostatic models, this study treats the glomerular filtration barrier (GFB) as an open thermodynamic system operating far from equilibrium. Aiming to overcome the delayed macro- outputs of traditional clearance biomarkers (creatinine and urea), this research introduces the "Glomerular Entropy Score" (Sglom) index to literature in order to instantaneously model real-time structural and ionic/osmotic disorder at the membrane level. Retrospectively analyzed records of 544 patients (N=544) were analyzed based on an empirical biophysical model across three independent software pipelines (Orange, RapidMiner, and JASP): Orange (3.38): The predictive power for long-term permanent renal damage was evaluated using Multilayer Perceptron Neural Network (MLP-NN), Naïve Bayes, Logistic Regression, and Random Forest (RF) algorithms; RapidMiner Studio (10.2): Model generalizability was verified using an isolated feature subset and 10-fold cross-validation; JASP (0.98.1): Descriptive and inferential biostatistical analyses, Student's t-test, and hypothesis validation via parametric matrices were conducted (JASP Team, 2024). Findings demonstrated that the calculated entropy index exhibits a non-linear, rightward-expanding chaotic tail structure against peak creatinine values. This condition proves thermodynamic disorder beyond critical bifurcation thresholds. Box plot analyses confirmed that local entropy production and clinical variation were significantly higher in the group with permanent renal failure (t = 2.149, df = 542, p = 0.032). In the Orange architecture, non-linear RF demonstrated superior performance (AUC = 0.800, F1 = 0.858, MCC = 0.518), while the RapidMiner RF model achieved an overall accuracy of 84.03% and a class sensitivity of 98.65% in predicting cases preserving filtration stability. The integration of non- equilibrium thermodynamic metrics with artificial intelligence and biostatistics offers a novel diagnostic framework for capturing GFB damage at an early stage before functional impairment settles.
Institution: Ministry of Health
Language: English
Subfield: Nephrology
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W7203684667
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