Shannon Entropy for Disease Forecasting: A Case Study on Kidney Graft Failure Prediction

J. Lpez Herrera (Spain)


Modelling, simulation, artificial intelligence, Diagnostics, systems experts, Shannon entropy, clinical analysis and neural networks


In this article, the viability of Shannon entropy as a metric for diagnosing an illness based on analytic tests of patients. In this case, the analysis measures biological variability of the kidney after a transplant [1]. The purpose of this article is to show a methodology based on Shannon entropy [2] that allows for diagnosing a possible graft loss in patients who have had a kidney transplant and helps to make a better medical decision to improve the life of the transplant patient. The patient data was provided by the nephrology department of the Hospital del Mar of Barcelona.

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