2020 № 1 Decision support services for the diagnosis and treatment of diseases and their practical application on the example of ckd 5d
The article clarifies the concept of DSS, shows the trend of development of new services in medicine. The article
describes the developed DSS‑service for issuing recommendations for the treatment and diagnostic process on the example of
complications in patients on permanent hemodialysis, in particular, correction of anemia. The information and logical architecture
of the service, the rating and trigger models used, the underlying knowledge base, and their relationship are described.
Based on the theory of decision-making, the principle of making recommendations is shown. Examples of integration of the
service with various information systems and other data sources are given. The analysis of the results of the implementation
of the DSS service for the correction of anemia in patients with CKD5D in the Nephrology and hemodialysis departments of
the medical clinical company Nefrosovet in integration with the information and analytical system for managing treatment and
diagnostic processes Maximus.
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.