2019 № 4 Prediction of recurrence in patients with Cushing’s disease after successful endoscopic transnasal adenomectomy: neural network model and its software implementation
Introduction. Due to the high frequency of recurrences in patients with Cushing’s disease after endoscopic transnasal adenomectomy (up to 55% in the 5 year period), it is important to develop a method for predicting recurrence of the disease based on a combination of factors. Мaterials and methods. The study included 219 patients who underwent endoscopic transnasal adenomectomy in 2007–2014.Over 3 years, remission persisted in 172 patients; relapse developed in 47 patients. The construction of artificial neural networks of various topologies was performed in the Statistica v. 13, and then software was developed for the best network.
Results. A highly efficient neural network (3-layer perceptron) was constructed, which allows predicting recurrence within 3 years or remission for at least 3 years. The sensitivity of the model is 74%, the specificity 97%, the positive predictive value 85%, the negative predictive value 93%. The predictors of the model are sex, age, duration of the disease, MRI type of adenoma, levels of adrenocorticotropic hormone and cortisol in blood in early postoperative period. Web-calculator was developed and is available to doctors for free practical use on http://medcalc.appspot.com/.
Сonclusion. The software implementing neural network is a quite effective tool for predicting recurrence and it will allow to perform personalized approach to management of patients who underwent neurosurgical treatment for the Cushing’s disease.
2017 № 4 Experience of the Development of the Software Package for Neural Network Diagnosis and Prediction of Diseases of Hepatopancreatoduodenal Zone
The article presents the experience of the internal development of a software package for diagnosis and forecasting diseases of hepatopancreatoduodenal zone based on the artificial neural network of multilayer perceptron type with hyperbol- ic tangent taken as an activation function. The article includes the characteristics of the analyzed data which is the set of risk factors for the development of peptic ulcer, cholecystitis and pancreatitis and substantiates the necessity for the application of automated control systems acting on the principles of artificial neural networks. The methods of operating of a multilayer per- ceptron are given, and there are proposed modifications intended to optimize the development of the software package and to solve a number of problems that arise during practical implementation of the system and during data preparation. A set of possible input and output parameters of the network, intended for its training, is proposed. The article contains the description of the practically developed user interface, intended to create, configure, train and clinically apply the artificial neural network, as well as to construct its graphs and statistically control its functioning.