AI-enabled predictive analytics and risk assessment in healthcare

Reliable digital physician assistant to identify high-risk patients

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Target audience

Whom Webiomed is intended for?

Healthcare managers

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

  • Automatic risk stratification of patients
  • Disease Prediction
  • Population monitoring for prevalence of risk factors
  • Monitiring of the proper EHR maintenance
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Clinicians

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

  • Clinical Decision Support System
  • Automatic determination of likelihood of disease development
  • Compliance with clinical guidelines
  • Reduction of time to analyze medical data
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Developers

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

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

Improvement of operational efficiency and scientific research

  • Production of datasets for RWD research
  • Identification of high-risk patients who require prescription of certain drugs
  • Analysis of the clinical practice of drug use
  • Search for unknown predictors in de-identified medical data
  • Machine Learning models production
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Insurance companies

Reduction of the cost of patient care and insurance claims

  • Identification of patients of high risk of disease, disability or treatment in medical organizations
  • Underwriting service for employees of an insurance company
  • Development of machine learning models
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How does it work?

Information gathering


While working with electronoc health record (EHR) a physician can send a reguest tо the CDSS. Then de-identified electronic health records are sent to Webiomed for analysis.

PHYSICIAN ASKS CDSS FOR ADVICE

EHR sends the data set to WEBIOMED

WEBIOMED Identifies risk factors and predicts complications

INDIVIDUAL RECOMMENDATIONS FOR BOTH PATIENT AND DOCTOR

Information analysis


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

Results on your screen


The answer is send to the information system that has requested the data analysis. The user can see the result in the EHR.

THE RESPONSE FROM WEBIOMED IS SENT BACK TO EHR

YOU CAN SEE THE RESULTS IN THE EHR

Webiomed presentation

Accumulated data

Digital twins

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Cases

0

Medical records

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Publications

Exploring 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.

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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.

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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

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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

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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.

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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.

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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.

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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

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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

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Media about us

Independent opinions about our product

19/10/2020 • portal "Republic"

In the fight against an an invisible enemy

22/07/2020 • Skolkovo

Company "К-SKAI" has attracted more than $1,8 million of private investment for developing the project Webiomed and entry to international markets.

Webiomed, a resident of Skolkovo has attracted more than $1,8 million of private investment

21/07/2020 • Ministry of Health of the Russian Federation

The pilot implementation of the clinical decision support system has been completed in medical organizations of the Kirov region.Developers carried out two scientific studies, analyzed the results and effectiveness of the system.

Artificial intellegent is being used in healthcare of Kirov region

19/06/2020 • EverCare

None

11/06/2020 • None

None

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

12/08/2019 • MIBS

Does Russia have artificial intelligence?

13/07/2019 • Rossiyskaya Gazeta

Can artificial Intelligence replace phycisians

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

How artificial intelligence can help physicians in their work

10/04/2019 • Rossiyskaya Gazeta

Artificial intelligence examined 30 thousand of patients

10/04/2019 • COMNEWS

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

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

06/04/2019 • Komsomolskaya pravda

Artificial Intelligence helps Yamal physicians

05/04/2019 • TOPNEWS

Can artificial Intelligence replace human doctors

05/04/2019 • SEVERPRESS

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

05/04/2019 • VESTI YAMAL

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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 in medicine and IT

Roman Novitsky Chief Executive Officer

Alexander Gusev Chief Business Development Officer, PhD

Andrey Salikov Сhief Сommercial Officer

Denis Gavrilov Chief Medical Officer

Semenov Head of analytics department, PhD

Anton Kovalyov Team Leader

Ilya Gelzin QA lead

Anna Guseva Project Analyst

Yuliya Pashkova Business analyst

Ivan Nikanov Analyst

Michail Kashin DevOps Engineer

Diana Lungu Business analyst

Scientific ADVISORS

Tatyana Kuznetsova Medical expert, Ph.D

Tokarev Sergey Medical expert, Ph.D

Alexander Ivshin Medical expert

Alexsander Rogov Expert in Mathematical Modeling, PhD

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

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

The Flagship University of the Republic of Karelia

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

The leading cardiology center in 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 disease prevention

Association of Clinical Pharmacologists

The largest organization of clinical pharmacologists in Russia

Awards and Achievements

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

Победитель в категории "Прорыв года" премии Digital Health Awards 2020
Winner in the nomination: "Breakthrough of the Year" Digital Health Awards 2020

Webiomed won the Breakthrough of the Year nomination in the Digital Health Awards. It is a Russian award for the best achievements in the field of digital medical technologies.

Победитель конкурса «Стартап-ралли»-2020 в номинации " Цифровая медицина"
The winner of Startup Rally

Призер конкурса "Безопасность медицинских изделий - на благо людей"
Webiomed is the winner of the "Safety of medical devices for the benefit of people" contest

According to the results of the competition, K-SkAI took the second place

Победитель в спецноминации "Персонализированная медицина" от Roche
The winner of personalized medicine special categories (company Roche)

Победитель конкурса конкурса инновационных проектов в области здравоохранения Sanofi совместно с Фондом " Сколково"
Winner of the projects competition in the field of healthcare organized Sanofi and the Skolkovo Foundation

Our Webiomed project was among the 3 winners of the competition.

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

Лауреат конкурса «Лучшее  ИТ  решение  для здравоохранения 2020»
Laureate of the contest "The best IT Solution for healthcare 2020"

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

Номинант Национальной  премии «Приоритет 2020»
A nominee of the National Prize "Priority 2020"

K-SKAI company, the developer of Webiomed predictive analytics system , was nominated for the award in the categories "Medicine and Health Care" and "Technology Startup".

Победитель конкурса инноваций «100 идей для развития Карелии».
Winner of the innovation competition “100 ideas for the development of Karelia”.

Лауреат конкурса «Лучшее  ИТ  решение  для здравоохранения 2019»
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 2019
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 developments in our product, in return, you use WEBIOMED for free in your work.

Contact Us Demo

Our blog

Artificial intelligence in medicine

06 Окт 2020  |   27

Interpreting Machine Learning Results

Albert Einstein: "If you can’t explain it to a six-year-old, you probably don’t understand it …

08 Июл 2020  |   26

Basic metrics of classification problems in machine learning

Each machine learning problem poses the question of evaluating the model results. Without the introduced criteria, …

01 Июл 2020  |   27

Cardiovascular diseases and COVID-19

In March of 2020, the Webiomed team received an order to add to the system …

05 Июн 2020  |   29

Application of NLP for extraction of information from Electronic Health Records

Electronic health records (EHR) represent the basis for the automation of a medical organization. Recently, …

28 Апр 2020  |   25

Why do we need risk scores and decision rules in Webiomed?

Recently, especially after the news on the registration of Webiomed as a medical device, we …

20 Сен 2019  |   1 052

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  |   1 069

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  |   1 062

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  |   1 258

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  |   920

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

03 Ноя 2020  |   37

WEBIOMED system wins Sanofi innovative projects competition

The Sanofi company together with the Skolkovo Foundation on October 30 summed up the results of the competition of healthcare innovative projects. Sanofi is one …

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30 Окт 2020  |   53

Webiomed is the winner of the "Safety of medical devices for the benefit of people" contest

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26 Окт 2020  |   27

We are preparing a new version of WEBIOMED

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23 Окт 2020  |   26

Webiomed project won the prestigious Startup Rally 2020 competition

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