Abstract
Medical diagnosis is one of the most tedious and complex processes that healthcare personnel face in their day-to-day life. To establish an adequate treatment, it is essential to carry out a correct and early evaluation of each patient. Occasionally, given the number of tests that need to be performed, this evaluation process can require a significant amount of time, and can negatively affect the patient’s recovery. The objective of this work is the development of a new software that, using Artificial Intelligence (AI), offers the healthcare professional support in the process of diagnosing the patient, as well as preventing the probability of suffering a certain disease, based on test information analytics and demographic information available. The system allows storing multiple models based on Deep Learning (DL), previously trained for the diagnosis of different diseases. These models allow predictions to be made based on available medical information. As a use case, one of these models has been successfully tested in diagnosing stroke events.
Highlights
Medical diagnosis is one of the fundamental tasks of physicians and essential and effective treatment
Artificial Neural Networks (ANN) are a Machine Learning (ML) and processing paradigm inspired by the functioning of the human nervous system
We conducted several experiments with five patients of different ages, in order to evaluate the accuracy of the results obtained and the evolution of the prediction of each patient in developing a stroke event based on the demographic values and laboratory tests performed
Summary
Medical diagnosis is one of the fundamental tasks of physicians and essential and effective treatment.
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