scholarly journals GW26-e0388 Adult Common Diseases Computer Diagnostic System

2015 ◽  
Vol 66 (16) ◽  
pp. C275
Author(s):  
Hongyuan Bai ◽  
Yajuan Ni ◽  
Xin Dong ◽  
Xiu Han ◽  
Tingzhong Wang ◽  
...  
Keyword(s):  
Diagnostic System ◽  
Common Diseases ◽  
Computer Diagnostic System

Distributed computer diagnostic system KardioVid on the base of the multi-agent technologies

2017 ◽  
Author(s):  
Oleg N. Bodin ◽  
Anatoly G. Ubiennykh ◽  
Anton S. Sergeenkov ◽  
Svetlana A. Balakhonova ◽  
Fagim K. Rakhmatullov ◽  
...  
Keyword(s):  
Diagnostic System ◽  
Multi Agent ◽  
Computer Diagnostic System

IMITATION MODELING OF MULTIAGENT TECHNOLOGY IN COMPUTER DIAGNOSTIC SYSTEM «CARDIOVID»

2019 ◽  
Author(s):  
O. N. Bodin ◽  
◽  
Z. I. Bausova ◽  
O. E. Bezborodova ◽  
A. G. Ubiennykh ◽  
...  
Keyword(s):  
Diagnostic System ◽  
Imitation Modeling ◽  
Computer Diagnostic System

scholarly journals Predicting various forms of endometriosis using artificial neural networks

2020 ◽  
Vol 35 (4) ◽  
pp. 143-149
Author(s):  
N. G. Sazonova ◽  
T. A. Makarenko ◽  
A. N. Narkevich
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Differential Diagnosis ◽  
Artificial Neural Networks ◽  
Mathematical Models ◽  
Computer System ◽  
Clinical Manifestations ◽  
Diagnostic System ◽  
Artificial Neural ◽  
Computer Diagnostic System

Introduction. Endometriosis is a difficult-to-diagnose pathology due to the diversity of clinical manifestations and the lack of high-precision markers necessary for rapid noninvasive diagnosis and timely administration of pathogenetically justified treatment.The aim of this work was to develop a computer system that allows us to assess the probability of endometriosis with various localizations in women, based on artificial neural networks.Material and Methods. The neural network mathematical models were constructed and tested based on data from 110 patients with morphologically pre-confirmed endometriosis. Patients were divided into training and test samples. The models were built based on anamnestic data and results of proteomic and enzyme immunoassays in blood plasma samples.Results and Discussion. In the course of the study, four mathematical models of neural networks were constructed to predict the presence or absence of endometriosis in a woman and its localization if present. Based on these mathematical models, a computer system “Differential diagnosis of endometriosis” was developed. This system allowed to assess the probability and localization of endometriosis in a patient based on parameters obtained as a result of neural network training.Conclusion. The developed computer diagnostic system allowed predicting the presence of endometriosis and its localization with a probability over 80%, depending on the predicted localization, based on data about the patient and the results of her examination. This system may be used for differential diagnosis of endometriosis from other diseases of the female reproductive system, as well as for differential diagnosis of various endometriosis localizations.

Sign in / Sign up

Export Citation Format

Share Document