Manuscript received January 19, 2022; revised April 1, 2022; accepted July 22, 2022.
Abstract—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.
Index Terms—Decision-making models, remote patient monitoring, Spiking Neural Network (SNN)
Sebastien Cohen, Florence Leve, Harold Trannois and Wafa Badreddine are with the MIS Laboratory - UPJV, 80000, Amiens, France.
Florian Legendre is with Evolucare Technologies, 80800, Villers-Bretonneux, France.
Cite: Sebastien Cohen*, Florence Leve, Harold Trannois, Wafa Badreddine, and Florian Legendre, "A Decision-Making Model Based on Spiking Neural Network (SNN) for Remote Patient Monitoring," International Journal of Machine Learning vol. 13, no. 2, pp. 88-96, 2023.
Copyright @ 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).