Objective: to develop an algorithm for choosing a treatment method for "difficult" cases of plantar fasciitis. Materials and methods. The clinical research included 40 patients with plantar fasciitis. These patients had so-called «hard-to-treat» cases of plantar fasciitis. These are: patients previously treated with local hormone injections or FSWT. There was no effect from the treatment. Depends on the clinical picture, the patients were divided into 3 groups: the main group (n=20) - patients with «hard-to-treat» cases, who underwent combined focused(FSWT) and radial shock wave therapy(RSWT); compare group (n=10) - patients who received only FSWT; control group(n=10) - patients who underwent local injection of hormones. Results. It was found that that the complex treatment of plantar fasciitis with radial and focused shock wave therapy in patients in «difficult cases» allows obtaining satisfactory results in 97% of patients 4 weeks after the end of treatment. This is significantly higher than with treatment with only focused shock wave and local hormone therapy (p<0.05). Moreover, hormone therapy was effective only in 4 (40%) patients who had previously received shock wave therapy. In patients with previous local injection of hormones, it was ineffective. Only focused shock wave therapy relieved pain syndrome in 6 (60%) patients. Conclusion. The study of the results of the treatment of «difficult» cases of plantar fasciitis by the method of radial and focused shock waves in combination, as well as only by the method of focused shock wave therapy and local injection with hormones will allow us to form an algorithm for the treatment of such patients to increase the number of satisfactory results.
The problem reviewed of building intelligent decision support systems for classification and prediction of the functional state of complex systems in the article. To predict the state of complex systems, hybrid decision modules with virtual flows are proposed, which reflect the hidden system connections between real and virtual data. The vector of informative features at the input of the hybrid decision module consists of two subsectors, the first of which corresponds to real flows, and the second - to virtual flows. Simulation modeling of classification processes using latent variables was performed, which allowed to evaluate the effect on the quality of classification of artificially introduced virtual flows. The structure of a neural network model with virtual recurrent-type streams is developed. The structure consists of N consecutively included neural network approximants. The outputs of the previous approximators are combined with the vector of in-formative attributes of the subsequent approximators, which allows forming virtual flows of different dimensions. A method is developed for the formation of non-linear models of virtual flows, characterized by the use of the GMDH-simulation method to obtain models of the influence of real flows on virtual flows, learned through nonlinear adalines. The method makes it possible to form a subvector of latent variables of unlimited dimension. Non-linear models of virtual flows are formed through a method based on the use of GMDH modeling. The method makes it possible to obtain neural network structures built on the basis of GMDH models and nonlinear adalines, which make it possible to form a subvector of latent variables of unlimited dimensionality.
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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.