AI-enabled predictive analytics in medicine
Reliable digital physician assistant to identify high-risk patients
Ready-made, trained solution to identify high-risk patients, to prevent morbidity and mortality.
Reliable digital assistant, trained on the results of evidence-based medicine and modern clinical guidelines.
Powerful artificial intelligence to evaluate medical data and identify risk factors without development costs.
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.
WEBIOMED identifies risk factors and predict the likelihood or even death of a patient. It generates individual recommendations for the prevention of the diseases.
The answer sends to the information system that has requested the data analysis before. The user sees the result on EHRs workplace.
Deep and machine learning models to improve risk prediction of cardiovascular disease using data extraction from electronic health records
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/ehz748.0670Читать
Prospects for the use of machine learning methods for predicting cardiovascular disease
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 modelsЧитать
Artificial intelligence for cardiovascular risks assessment
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.Читать
The basic recommendations for the creation and development of information systems in health care based on artificial intelligence
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.Читать
Artificial Intelligence in Medicine and Healthcare
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.Читать
Prospects for neural networks and deep machine Learning in creating health solutions
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 futureЧитать
Clinical Decisions Support in medical information systems of a medical organization
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 doctorsЧитать
AI will help Yamal physicians to identify dangerous diseases at an early stages
AI will help Yamal physicians to identify dangerous diseases at an early stages
How artificial intelligence can help physicians in their work
Artificial Intelligence in Muravlenko helped to detemine dozens of patients at risk
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
National Base of medical knowledge
The Association of Developers and Users of Artificial Intelligence Systems in Medicine
Association of Clinical Pharmacologists
This is the largest organization of clinical pharmacologists in Russia.Established in 2009
For the CDSS "Webiomed" in the framework of the international congress #ITM2019
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
One of the most promising areas of digital health development is artificial intelligence. There are ...
Although the AI pilot projects are still rare, the vast majority of Russians healthcare leaders are aware ...
An annotation of the report “Artificial Intelligence in Russian Medicine": Decision Support Systems” published on ...
The key area of digital healthcare is the use of Clinical Decision Support Systems (CDSS), ...
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., ...Continue...