AI-enabled predictive analytics and risk score in healthcare

Reliable digital physician assistant to identify high-risk patients

Contact Us Demo

Target audience

Whom Webiomed is intended for?

Healthcare managers

Reduction of the morbidity and mortality via identification and epidemiologically monitoring of high-risk patients

  • Automatic risk stratification of patients
  • Disease Prediction
  • Population monitoring of prevalence of risk factors
  • Control of the correctness of conducting EHR by doctors
Contact Us

Clinicians

Reduction of medical errors and time for EHR analysis of and personal recommendations for the prevention of diseases

  • Clinical Decision Support Systems
  • Automatic determination for likelihood of diseases development
  • Compliance with clinical guidelines
  • Reduction of time time to interpret medical data
Contact Us

Developers

Additional competitive advantages due to powerful artificial intelligence in the evaluation of patient medical data

  • Ready service for evaluation of medical data
  • Improvment of the attractiveness of EHR in eyes of doctors and managers
  • Elimination of the need to register MIS ( medical information system) as a medical device
Contact Us

Рharma

Improvement of work efficiency and scientist researchers

  • Production of datasets for RWD research
  • Identification of high-risk patients for prescribing special drugs
  • Analysis of the clinical practice for use of drugs
  • Search for unknown predictors in de-identified medical data
  • Machine Learning model production
Contact Us

Insurance company

Reduction of patient care and insurance coverage

  • Identification of patients at high risk of disease, disability / treatment in medical organizations
  • Underwriting service for employees of an insurance company
  • Reduction of patient care and insurance coverage
Contact Us

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

Accumulated data

Digital twins

0

Causes

0

Medical records

0

Publications

We are exploring the topic of artificial intelligence

Врач. Научно-практический журнал

CARDIOVASCULAR DISEASES PREDICTION BY INTEGRATED RISK FACTORS ASSESSMENT BY MEANS OF MACHINE LEARNING

Authors: D. Gavrilov; L. Serova, I. Korsakov, A. Gusev, R. Novitsky, T. Kuznetsova

D. Gavrilov; L. Serova, I. Korsakov, A. Gusev, R. Novitsky, T. Kuznetsova CARDIOVASCULAR DISEASES PREDICTION BY INTEGRATED RISK FACTORS ASSESSMENT BY MEANS OF MACHINE LEARNING // Vrach.2020.-№5-P.41-45

Aim. To develop a model by machine learning to predict the risk of cardiovascular diseases (CVD) and validate the model using Russian medical data. Materials and methods. The data set was obtained from the Framingham study, consisting of 4,363 patients without CVD, 852 (19.5%) of which died of myocardial infarction and stroke within 10 years of observation. Incoming model features: gender, age, systolic blood pressure, cholesterol, smoking, body mass index, heart rate. The original data set was divided into 2 parts: the training data set (80% of the records) and the validate data set (the remaining 20%). Additionally, the model was evaluated by an external data set included 411 depersonalized patient data from the Russian citizens. Results. The WML.CVD.Score model was created by the serial neural network with one input, two hidden and one output layer. Accuracy results on a training dataset: Accuracy 81.15%, AUC 0.80. The same indicators on the validate data set were: Accuracy 81.1%, AUC 0.76. Test results for the test data set: Accuracy 79.07, AUC 0.86. On the Russian test data, the AUC for the SCORE scale was 0.81 versus 0.86 for the developed model, which showed the validity of the use of machine learning in order to increase the predictive model. Conclusion. The developed model has demonstrated high accuracy to CVD predicting in both internal and external validation. The model can be used in medical practice for patients in Russia.

Read
PROCEEDING OF THE 26TH CONFERENCE OF FRUCT ASSOCIATION, April 2020.

Feature Extraction Method from Electronic Health Records in Russia

Authors: Gavrilov D., Gusev A., Korsakov I., Novitsky R., Serova L.

Gavrilov D., Gusev A., Korsakov I., Novitsky R., and Serova L., "Feature Extraction Method from Electronic Health Records in Russia", in Proceedings of the FRUCT’26, ISSN 2305-7254, ISBN 978-952-69244-2-7, pp. 497-500, April 2020

The medical language is the basis of the electronic medical records (EHR), and up to 70 percent of the information in these records were writing in natural language, in the free text part. The last few years have seen a surge in the number of accurate, fast, publicly available name entity recognition (NER) parsers. At the same time, the use of NER parsing in natural language processing (NLP) applications has increased. It can be difficult for a non-expert to select a good “off-the-shelf” parser. We present a method of using statistical NER parsers on a medical corpus of Russian. We developed a new tool that gives a convenient way to extract NER from unstructured medical documents.

Read
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

Authors: Korsakov I., Gusev A., Kuznetsova T., Gavrilov D., Novitskiy R.

Korsakov I., Gusev A., Kuznetsova T., Gavrilov D., Novitskiy R. Deep and machine learning models to improve risk prediction of cardiovascular disease using data extraction from electronic health records // European Heart Journal, Volume 40, Issue Supplement_1, October 2019, ehz748.0670

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: https://doi.org/10.1093/eurheartj/ehz748.0670

Read
Information technologies for the Physician • №3 2019

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

Authors: Gusev A. V., Gavrilov D. V., Korsakov I. N., Serova L. M., Novitsky R. E., Kuznetsova T.Yu.

Gusev A. V., Gavrilov D. V., Korsakov I. N., Serova L. M., Novitsky R. E., Kuznetsova T.Yu. Prospects for the use of machine learning methods for predicting cardiovascular disease // Information technologies for the Physician. 2019 №3. p. 41-47

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

Read
Journal of Telemedicine and E-Health • №3 2018

Artificial intelligence for cardiovascular risks assessment

Authors: Gusev A.V., Kuznetsova T.Yu., Korsakov I.N.

Gusev A.V., Kuznetsova T.Yu., Korsakov I.N. Artificial Intelligence in Risk Assessment of Cardiovascular Diseases // Journal of Telemedicine and E-Health. 2018. No. 3 (8). S. 85-90.

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.

Read
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

Authors: Gusev A.V., Pliss M.A.

Gusev A.V., Pliss M.A. The main recommendations for the creation and development of information systems in healthcare based on artificial intelligence // Doctor and information technology. 2018. No3. - Page 45-60

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.

Read
INFORMATION SOCIETY* №4 2017

Artificial Intelligence in Medicine and Healthcare

Authors: Gusev A.V., Dobridnyuk S.L.

Gusev A.V., Dobridnyuk S.L. Artificial Intelligence in Medicine and Health Care // Information Society, No. 4-5, 2017. pp. 78-93

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.

Read
Information technologies for the Physician *№3 2017

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

Authors: Gusev A.V.

Gusev A.V. Prospects for neural networks and deep machine learning in creating solutions for healthcare // Physician and information technology. 2017. No3. Page 92-105

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

Read
Information technologies for the Physician * №2 2017

Clinical Decisions Support in medical information systems of a medical organization

Authors: Gusev A. V., Zarubina T. V.

Gusev A.V., Zarubina T.V. Support for the adoption of medical decisions in the medical information systems of a medical organization // Doctor and information technology. 2017. No2. Page 60-72

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

Read

Media about us

The independent opinions about our product

22/04/2020 • None

Artificial intelligence from Skolkovo resident was registrated as a medical device

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

None

11/06/2020 • None

None

19/06/2020 • EverCare

None

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

Andrey Salikov Сhief Сommercial Officer

Denis Gavrilov Chief Medical Officer

Anton Kovalyov Team Leader

Serova Larisa Head of analytics department, PhD

Guseva Anna Project Analyst

Pashkova Yuliya Business analyst

Ivan Nikanov Analyst

Kashin Michail DevOps Engineer

Scientific ADVISORS

Kuznetsova Tatyana Medical expert, Ph.D

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

Tokarev Sergey Medical expert, Ph.D

Rogov Alexsander Expert in Mathematical Modeling, PhD

Our partners

We actively cooperate with the professional community

Skolkovo

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

Petrozavodsk State University

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

Federal State budget organization National medical research center of cardiology of the Ministry of Healthcare (Russia)

One the leading Cardiology Center of Russia

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

Association of Clinical Pharmacologists

This is the largest organization of clinical pharmacologists in Russia.

Awards and Achievements

Первый искусственный интеллект для здравоохранения  в России, зарегистрированный как медицинское изделие
The software using artificial intelligence for healthcare was registered as a medical device for the first time in Russia

Победитель коммерческого трека AstraZeneca Skolkovo StartUp Challenge 2020
AstraZeneca Skolkovo StartUp Challenge 2020

Победитель в номинации «Цифровые решения для здравоохранения»
Winner in the nomination: "Digital Healthcare Solutions"

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"

Affiliate program

Connect to WEBIOMED for free and without restrictions! Sign up now

Owners of high-quality structured electronic medical data are invited to join the affiliate program. We include your work in our product, in return, you use WEBIOMED for free in your work.

Contact Us Demo

Our blog

Interesting about artificial intelligence

20 Сен 2019  |   660

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  |   751

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  |   780

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  |   961

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  |   663

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. ...

All articles

Our news

WEBIOMED project development stages

23 Апр 2020  |   169

Webiomed became the first registrated Russian software as a medical device in the field of artificial intelligence in healthcare

April 3, 2020 The Federal service for healthcare supervision of the Russian Federation (Roszdravnadzor)  registered  CDSS Webiomed  as a medical device. Webiomed became the first ...

Continue...
09 Окт 2019  |   464

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

Read more...

09 Окт 2019  |   422

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

Read more...

02 Окт 2019  |   283

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

Read more...
All news

Mailing list

Do you interested in digital healthcare and artificial intelligence for medicine? Join our mailing list!