Using the method of artificial neural networks for integration into the decision support system as a tool for optimizing outpatient management of patients with chronic obstructive pulmonary disease

Abstract

Objective: to evaluate the possibility of using artificial neural networks for integration into the medical decision support system as an optimization of outpatient management of patients with COPD.Materials and methods: a dynamic followup of 150 patients with chronic obstructive pulmonary disease, registered at the dispensary for the underlying disease, who completed the outpatient stage of pulmonary rehabilitation after a moderate exacerbation was carried out. The material of the study was a universal questionnaire of 69 indicators, including anamnesis, clinic, laboratory and instrumental diagnostics. A four-layer neural network has been created: the first two layers — 69 neurons, the third layer — 34 neurons and the last layer — 3 neurons.Results: the software was used in the Java programming language using the Encog 3.4 module.Conclusion: the use of the capabilities of artificial neural networks for integration into the medical decision support system in the outpatient management of patients with chronic obstructive pulmonary disease has shown high specificity. The predictive model is implemented in the form of a computer program: "The program for predicting an unfavorable outcome, the development of cardiovascular complications and the effectiveness of rehabilitation measures in patients with chronic obstructive pulmonary disease (CardioRisk)" and was introduced into the work of outpatient polyclinic institutions in Rostov-on-Don.