Abstract
Objective. The article provides information on how to improve the forecast of the early postoperative period by additional individualization of anesthetic management of patients during emergency surgical interventions on the gallbladder using artificial neural network technologies. Materials and methods. The course of combined anesthesia and the features of the postoperative period were analyzed in 92 patients with an endoscopic cholecystectomy performed for urgent indications. The prediction of the variant of the postoperative stage of hospitalization was realized using the analysis of the significance of 20 different-modal variables selected for the description of patients using fuzzy logic technologies. The possibility of changing the forecast to a more favorable one was achieved on the basis of the developed algorithm for evaluating the results of training neural networks on the Neuro Pro 0.2 neuroimitator. Results. According to the generally accepted criteria, all patients had endoscopic cholecystectomy and anesthesia wit out complications. At the postoperative stage, 2 groups of persons were identified - with the expected short hospitalization (72 cases - 6.7±2.1 days) and with the clinic, which led to its reliable prolongation (20 cases - 12.2±3.5 days). It has been shown that the use of a neural network approach makes it possible with a confidence of more than 80% to assume cases with a high probability of postoperative disorders and in half of such patients to improve the prognosis within the framework of neural network technology and the developed algorithm for selecting the severity of the selected 5 variable factors related to the method of conducting anesthesia. Conclusion. Neural network technology makes it possible to predict cases with individual “unpredictable” responses to surgical trauma. Assessing the significance of the factors used and varying their severity create the basis for the individualization of anesthetic management of patients, prevention of postoperative reactions and a reduction in the period of hospitalization.
Highlights
Цель исследования – улучшение прогноза раннего послеоперационного периода путем дополнительной индивидуализации анестезиологического ведения больных во время экстренных хирургических вмешательств на желчном пузыре за счет технологий искусственных нейронных сетей
Average significance of Network1 and 6 factors used to recognize the variant of the postoperative period
For citation: Saraev I.A., Mishustin V.N. Possibilities for forecasting and preventing early postoperative complications
Summary
Цель исследования – улучшение прогноза раннего послеоперационного периода путем дополнительной индивидуализации анестезиологического ведения больных во время экстренных хирургических вмешательств на желчном пузыре за счет технологий искусственных нейронных сетей. Прогноз варианта послеоперационного этапа госпитализации реализовали с помощью анализа значимости избранных для описания больных 20 разномодальных переменных с привлечением технологий нечеткой логики. Что использование нейросетевого подхода позволяет с уверенностью более 80% предполагать случаи с высокой вероятностью послеоперационных расстройств и у половины таких больных улучшить прогноз в рамках технологии нейронных сетей и разработанного алгоритма подбора степени выраженности избранных 5 переменных факторов, относящихся к методике проведения анестезиологического пособия. В связи с вышесказанным целью исследования стало создание основы для максимально возможной индивидуализации ведения больных во время хирургических вмешательств на желчном пузыре, в том числе – для оптимизации течения раннего послеоперационного периода за счет анализа значимости избранных для прогнозирования переменных с привлечением технологий нейронных сетей
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