A Decision-Making Model Based on Spiking Neural Network (SNN) for Remote Patient Monitoring

Nowadays, the medical sector faces several challenges due to different factors including the increase in the number of patients to be taken care of, the economic crisis and the saturation of hospitals. Hence, hospital administrations aim to develop new strategies to handle these issues as remote patient monitoring. In this context, we propose a decision-making Spiking Neural Network (SNN) model regarding patient health conditions to integrate to patient monitoring systems. Our model offers, based on the measurements of the physiological parameters of the patient, a feedback of the patient's health condition and a raising of the alert if necessary. To do so, we construct an SNN model that represents the rules provided by a group of doctors and that allow this model to be representative of one patient. The results obtained by our model as well as those of a rule-based model validated by physicians have an error rate of less than 10%. Our goal is to reduce this error rate associating the two models and not to put the two models in competition.