AI-enabled predictive analytics in medicine

Reliable digital physician assistant to identify high-risk patients

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WEBIOMED for healthcare managers

Ready-made, trained solution to identify high-risk patients, to prevent morbidity and mortality.

  • Automatic risk stratification of patients
  • A more efficient organization of preventive work aimed at personal group of patients with a high risk of complications and death
  • The ability to route patients depending on the assessment obtained
  • Reduced morbidity and mortality
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WEBIOMED for clinicians

Reliable digital assistant, trained on the results of evidence-based medicine and modern clinical guidelines.

  • Automatic Identification of Risk Factors
  • Automatic determination of the likelihood of developing a disease
  • Compliance with clinical practice guidelines
  • Reduced time of the patient risk assessment
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WEBIOMED for developers

Powerful artificial intelligence to evaluate medical data and identify risk factors without development costs.

  • The addition of medical decision support functions
  • Ready service for evaluating EHR and to identify the risk factors
  • Reducing the costs of development of medical information system
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How does it work?

Gathering of information

During working with electronic medical record (EHR) physicians use the special command to call CDSS. The de-identified electronic health records are send to CDSS Webiomed for assessment on this command.

Physician asks for advice CDSS

EHR sends the data set to WEBIOMED

WEBIOMED Identifies risk factors and predicts complications.

Individual recommendations for the patient and the doctor are formed.

Analysis of information

WEBIOMED identifies risk factors and predict the likelihood or even death of a patient. It generates individual recommendations for the prevention of the diseases.

Results on your screen

The answer sends to the information system that has requested the data analysis before. The user sees the result on EHRs workplace.

The respond back are send by WEBIOMED to EHR

You can see the results on your EHR workplace

Webiomed presentation


We are exploring the topic of artificial intelligence

European Heart Journal • Volume 40 • Issue Supplement_1 • October 2019

Deep and machine learning models to improve risk prediction of cardiovascular disease using data extraction from electronic health records

Авторы: Kuznetsova TatyanaNovitsky RomanGusev AleksanderDenis GavrilovIgor Korsakov

Advances in precision medicine will require an increasingly individualized prognostic evaluation of patients in order to provide the patient with appropriate therapy. The traditional statistical methods of predictive modeling, such as SCORE, PROCAM, and Framingham, according to the European guidelines for the prevention of cardiovascular disease, not adapted for all patients and require significant human involvement in the selection of predictive variables, transformation and imputation of variables. In ROC-analysis for prediction of significant cardiovascular disease (CVD), the areas under the curve for Framingham: 0.62–0.72, for SCORE: 0.66–0.73 and for PROCAM: 0.60–0.69. To improve it, we apply for approaches to predict a CVD event rely on conventional risk factors by machine learning and deep learning models to 10-year CVD event prediction by using longitudinal electronic health record (EHR). Acces:

Information technologies for the Physician • №3 2019

Prospects for the use of machine learning methods for predicting cardiovascular disease

Авторы: Kuznetsova TatyanaNovitsky RomanGusev AleksanderDenis GavrilovIgor KorsakovSerova Larisa

Morbidity and mortality from cardiovascular diseases (CVD) has remained the leading rate in recent decades worldwide. Primary prevention methods based on the management of cardiovascular risk factors are most effective in reducing the burden of CVD. In preventive medicine for risk management of CVD use the riskometers – scales that was obtained as a result of long prospective studies. But the practical application of the developing scales has showed the limitations in the forecast accuracy. Machine learning makes it possible to improve the accuracy of cardiovascular risk prediction due to nonlinear relationships of their deeper adjustment between risk factors and disease outcomes. 2236 patients’ data were used. We trained the model on the features used in the Framingham scale construction. We compared the resulting model and the Framingham scale for the accuracy of the cardiovascular event prediction. Thus, according to the ROC analysis for the Framingham scale, the indicators are as follows: precision Accuracy: 70,0%, the AUC: 0.59. At the same time for the model obtained using machine learning similar indicators were: Accuracy: 78,8%, AUC: 0.84. Thus, the use of machine learning algorithms including deep learning algorithms can significantly improve the accuracy of cardiovascular risk prediction of trained models

Journal of Telemedicine and E-Health • №3 2018

Artificial intelligence for cardiovascular risks assessment

Авторы: Kuznetsova TatyanaGusev AleksanderIgor Korsakov

Cardiovascular disease (CVD) is one of the leading causes of death and disability in most countries of the world, including Russia. According to Russian Federal State Statstics Service (Rosstat) in 2016, 904 thousand people died from diseases of the circulatory system in Russia, which amounted to 47.8% in the structure of the causes of mortality. A significant proportion of this morbidity and mortality could be prevented through early diagnosed and prevention strategies, both for people with established disease and for those at high risk of developing disease.

Information technologies for the Physician * №3 2018

The basic recommendations for the creation and development of information systems in health care based on artificial intelligence

Авторы: Gusev Aleksander

Artificial intelligence is becoming one of the main drivers in solving serious problems of medicine and health, such as inadequate resources, further improving efficiency, quality and speed of work. All over the world, more and more solutions are being developed in this area. However, the more new products appear, the more questions and problems arise. The work analyzes some foreign publications and research results, which studied the main problems associated with the creation and implementation of artificial intelligence in health care. As a result of the analysis, a number of practical recommendations were formulated that will help increase the likelihood of successful creation and introduction of such products in the practical link of health.


Artificial Intelligence in Medicine and Healthcare

Авторы: Gusev Aleksander

The article discusses the prospects for using artificial intelligence technologies in medicine and healthcare. The history of artificial intelligence development is described. The technologies of machine learning and neural networks are analyzed. The review of already implemented artificial intelligence projects is provided. The forecast of the most promising directions of the artificial intelligence technologies development, according to the authors, for the near future is given.

Information technologies for the Physician *№3 2017

Prospects for neural networks and deep machine Learning in creating health solutions

Авторы: Gusev Aleksander

The paper gives an overview of the prospects of using neural networks and deep machine learning in the creation of artificial intelligence systems for healthcare. The definition and explanations on the technologies of machine learning and neural networks are given. The review of already implemented artificial intelligence projects is presented, as well as the forecast of the most promising directions of development in the near future

Information technologies for the Physician * №2 2017

Clinical Decisions Support in medical information systems of a medical organization

Авторы: Gusev Aleksander

In the article the review of various possibilities of support of acceptance of medical decisions in medical information systems of the medical organizations is presented. The description of functional requirements and prospects in terms of increasing the effectiveness of medical information systems in the informatization of clinical work of doctors


Media about us

The independent opinions about our product

12/08/2019 • Zdrav.fom

AI will help Yamal physicians to identify dangerous diseases at an early stages

13/07/2019 • Rossiyskaya Gazeta

Can artificial Intelligence replace phycisians

09/04/2019 • Ministry of Health of Russian Federation

AI will help Yamal physicians to identify dangerous diseases at an early stages

09/04/2019 • Yamal government

Yamal government decides to expand AI implemetation

10/04/2019 • Rossiyskaya Gazeta

Artificial intelligence examined 30 thousand of patients

12/08/2019 • MIBS

Does Russia have artificial intelligence?

06/05/2019 • National Technology Initiative (NTI)

How artificial intelligence can help physicians in their work

06/04/2019 • Komsomolskaya pravda

Artificial Intelligence helps Yamal physicians

05/04/2019 • TOPNEWS

Can artificial Intelligence replace human doctors

10/04/2019 • COMNEWS

Yamal is the pilot regions that began to implement AI in healthcare

05/04/2019 • SEVERPRESS

Artificial Intelligence in Muravlenko helped to detemine dozens of patients at risk

05/04/2019 • VESTI YAMAL


About Us

We decided to develop CDSS (clinical decision support system) in summer, 2018.

The idea of creating this system is to support, help physicians and health care organizers in making more effective decision for improving quality of patients care and mortality lessen

We have well -balanced team of the experts in medicine, machine learning and information technologies.

Our goal is to prevent the diseases

Our team

The team of professionals from medicine and IT

Novitsky Roman Chief Executive Officer

Gusev Aleksander Chief Business Development Officer, PhD

Denis Gavrilov Chief Medical Officer

Igor Korsakov Lead Data Science Analyst, PhD in Math & Computer Science

Serova Larisa Project Manager, PhD

Guseva Anna Project Analyst

Anton Kovalyov Team Leader

Victoria Gordeeva QA engineer

Kashin Michail DevOps Engineer

Tareev Pavel Developer

Nikolaev Dmitry Developer

Malyshev Denis Software Test Engineer

Scientific ADVISORS

Kuznetsova Tatyana Medical expert, Ph.D

Tokarev Sergey Medical expert, Ph.D

Rogov Alexsander Expert in Mathematical Modeling, PhD

Our partners

We actively cooperate with the professional community

National Base of medical knowledge

The Association of Developers and Users of Artificial Intelligence Systems in Medicine

Medical Prevention Center

YNAO Health Organization for prevent diseases


The Skolkovo Innovation Center is a high technology business area in Russia

Association of Clinical Pharmacologists

This is the largest organization of clinical pharmacologists in Russia.Established in 2009

Petrozavodsk State University

Petrozavodsk State University is the Flagship University of the Republic of Karelia

Awards and Achievements

Лауреат конкурса «Лучшее  ИТ  решение  для здравоохранения»
Laureate of the contest "The best IT Solution for healthcare"

For the CDSS "Webiomed" in the framework of the international congress #ITM2019

Призер в номинации : «Лучший инновационный проект»
Prize winner in the nomination: “Best Innovative Project”

The project "Implementation of artificial intelligence systems for medicine" (Yamal-Nenets Autonomous Okrug) won the nomination "Best Innovative Project" of the PROF-IT.2019 contest

Номинант на гран-при как «Стартап года» премии Digital Health Awards
Grand Prix nominee as Startup of the Year at the Digital Health Awards

CDSS Webiomed was among the nominees for the Grand Prix of the Digital Health Awards as "Startup of the Year"

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Our blog

Interesting about artificial intelligence

20 Сен 2019  |   389

The map “Artificial Intelligence in Russian Health Care”

One of the most promising areas of digital health development is artificial intelligence. There are ...

16 Июл 2019  |   538

What Russian healthcare managers are thinking about artificial intelligence

Although the AI pilot projects are still rare, the vast majority of Russians healthcare leaders are aware ...

05 Окт 2018  |   629

Report: "Artificial Intelligence in Russian Medicine: Decision Support Systems"

An annotation of the report “Artificial Intelligence in Russian Medicine": Decision Support Systems” published on ...

28 Авг 2018  |   802

About registration of Clinical Decision Support Systems as a Medical Device

The key area of ​​digital healthcare is the use of Clinical Decision Support Systems (CDSS), ...

30 Июн 2018  |   543

About the session “Artificial Intelligence in Medicine. Digital Health"

On June 26-28, the All-Russian Consilium of Honored Doctors of Russia was held in Moscow. ...

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Our news

WEBIOMED project development stages

09 Окт 2019  |   340

Prospects for the using of machine learning methods for predicting cardiovascular disease

The article "Prospects for the using of machine learning methods for predicting cardiovascular disease" by authors Gusev A. V., Gavrilov D. V., Korsakov I. N., ...

09 Окт 2019  |   319

Webiomed - grand prix nominee as Startup of the Year at the Digital Health Awards


02 Окт 2019  |   163

Our experts took part in the international forum IMDRF "Artificial Intelligence in Healthcare. Opportunities and Challenges"


30 Сен 2019  |   340

We became a Skolkovo resident!

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