TSU student will teach neural networks to recognize diseases by EKG
Vladimir Andryushchenko, a postgraduate student at the TSU Institute of Applied Mathematics and Computer Science, develops methods and algorithms that will help to determine and predict changes in the patient's condition based on medical signals. He is creating a large library of medical data required for machine learning by a computer model that will verify diseases using electrocardiogram (EKG) signals.
- A significant part of the data used for diagnostics is still in analog form, which significantly reduces the possibility of fully analyzing them, - says Vladimir Andryushchenko. - This problem can be solved by switching to digital. The accumulated data for one patient or group can contain useful information not only about the current state of health but also about the beginning of critical changes in the human body.
As he notes, there are now examples of successful application of machine learning methods to detect a specific disease or class of diseases, but there is no universal way to detect a wide range of diseases. To create such an algorithm, we need a huge training sample, which will have a large number of patterns - repeating elements characteristic of each class of disease.
Read more: http://en.tsu.ru/news/tsu-student-will-teach-neural-networks-to-recognize-diseases-by-ekg/
