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This paper presents the results of studying the effectiveness of the developed method for diagnosing the functional state of the dental system. The results indicate that the accuracy of the method is about 80%. The results of previous studies are reviewed and the prospects for diagnosis of the functional state of the dental system as a research and practical trend in dentistry are outlined. The physiological regularities of the dental system functioning and relationship between its functional status and the autonomic nervous and postural systems prompt a new trend of research, functional diagnosis in dentistry and interpretation of the findings of functional studies. The prospects of functional diagnosis in dentistry are discussed.

Health management, or in other words health optimization, is coming to the forefront in most of the world’s countries. Almost every person has a certain predisposition to the development of chronic diseases, which is determined by corresponding risk factors. Some of these risk factors can be managed in order to minimize the risk of disease, which promotes health optimization. The intelligent system presented here performs the task of monitoring manageable risk factors, assessing their impact, and formulating recommendations to reduce this impact. The performance of the system is demonstrated on the examples of myocardial infarction, stroke and depression. The intelligent system evaluates the risks of diseases on the basis of individual risk factor values and provides recommendations to reduce these risks. A health manager who possesses additional information about specific aspects of the person’s health can adjust the recommendations issued by the system.

Automated extraction of cell populations’ immunosuppressive properties from research articles is a basic problem, which requires specialized methods and tools for meta-analysis of publications. It is necessary to extract information about specific types of cells, their roles in the text, detect the immunosuppressive properties of cell populations, and certain types of relationships. It is also crucial to filter out those texts, which describe immunosuppressive features of different chemical compounds or drugs. Typically, efficient automatic information extraction requires a relatively large set of samples or marked-up texts. The paper presents a novel information extraction method that can be useful for such an analysis. This method uses external linguistic resources and can be trained on limited corpora. Namely, the method combines medical ontologies, rich unsupervised lexis representations (Fasttext word embeddings) with rule-based entity and relationship extraction, and supervised machine learning-based post-filtering. The developed method allows one to extract information about the target cells (for “in vitro” experiments), effector cells, diseases ("in vivo”), and arbitrary descriptions of immune suppression. In the paper, we also present a manually labeled corpus to train immunosuppressive information extraction methods. That corpus contains texts of 330 PubMed Central abstracts. The experiments on that corpus show the method has relatively high evaluation scores on the labeled dataset. Therefore, the proposed method makes it possible to identify descriptions of the immunosuppressive properties of cell populations in biomedical texts with sufficiently high quality. In the future, the method can be applied to perform an automatic meta-analysis of research in immunosuppressive cell therapy.

This paper describes the creation of a knowledge base for an intelligent healthcare management system dealing with cases of stroke, myocardial infarction and depression. The main purpose of the knowledge base is to discover and evaluate risk factors and situations which can lead to such diseases, and to enable the formation and support of a preventative measures plan. The present version of the knowledge base is implemented using a heterogeneous semantic network approach and utilizes expert opinions about risk factors and events influencing an individual’s health. Data includes genetic predisposition, lifestyle, and external environment. Data is compiled with the aid of questionnaires, mobile devices, case histories and information from social media. Information from social media is analyzed using data and text mining methods with the goal of evaluating the user’s condition. All of the data obtained is accumulated in a single database. The knowledge base establishes risk factors, including changes in those factors over the course of time, and circumstances or events which might precipitate the emergence of pathology. Hypotheses are generated about the current state of the user’s health, the active risk factors which created conditions for the onset of disease, and circumstances which might produce an increase or decrease in risk factors. Prophylactic measures to reduce those risks are suggested through analysis of the hypotheses generated. Recommendations regarding prophylactic measures are formed with the aid of the knowledge base, the user case-library, and collaborative filtering methods. Recommendations are based on 4P medicine, which re-quires mandatory participation of system users in maintaining their health.

The paper describes the technology of forming a list of personalized preventive recommendations. The technology consists of the following main components: human health state, data acquisition module, database, knowledge base, and solver with output explanation. In the version presented, this technology allows one to assess the risks of stroke, myocardial infarction and depression, contains more than two hundred risk factors for these diseases and more than twenty preventive recommendations. Training for this version was based on automated analysis of a large number of publications and expert knowledge.

The paper describes knowledge base principles and method for disease risk evaluation that were used for intelligent healthcare management system creation. The present version of the knowledge base is implemented using a heterogeneous semantic network approach and utilizes expert opinions about risk factors and events influencing an individual’s health. Data includes genetic predisposition, lifestyle, and external environment. Data is compiled with the aid of questionnaires, mobile devices, case histories and information from social media. Information from social media is analyzed using data and text mining methods with the goal of evaluating the user’s condition. All of the data obtained is accumulated in a single database. The method for risk evaluation and preventive measures plan hypotheses generation is based on an argumentation reasoning algorithm that is modified to the task at hand. All prevention recommendations are based on the principles of P4 medicine. The current version of the system is based on expert knowledge obtained by automated monitoring and analysis of a large number of publications and recommendations on this topic.

This paper presents an ontological approach to generating automated medical technological records (MTRs). The medical organizational TR meta-ontology has been developed based on the paper variant of 63 technological records that are used at the Medical Center of the Bank of Russia. The paper describes the key elements of an automated MTR. The MTR ontology for bronchial asthma attack is used as an example.

Multifactorial nature of human health and need in personifying the approach to each person leads to the fact that full implementation of healthy life style (HLS) technologies is possible only on the basis of artificial intelligence technologies, widely implemented in the preventive medicine via modern Internet technologies. Modern computer systems allow considering simultaneously big data of separate factors in health assessing and selecting of individualized recommendations for personal HLS. This article presents a concept and a structure of intelligent Internet technology for personalized health-saving support, which allow assessing health and preparing individual optimal recommendations for HLS.

Cell-based immunotherapy is a promising approach for the treatment of chronic infections, autoimmune disorders, and malignant tumors. There are many strategies of cell-based immunotherapy of cancer; these include injection of various immune effector cells, propagated and «trained» in a cell culture. Alternatively, cells presenting tumor antigens on their surface in a form recognized by the immune system can be used to achieve a therapeutic effect. The research results in this field are presented in thousands of texts, and their manual analysis is very complicated. We have developed an approach for automated text analysis in this area of biomedical science. Here we present the first results of the automated analysis of the data extracted from abstracts of scientific articles available in PubMed. These results demonstrate the associations between types of tumors and the most commonly used methods of their cell-based immunotherapy., Клеточная иммунотерапия это перспективный подход к лечению хронических инфекций, аутоиммунных нарушений и злокачественных опухолей. Существует множество стратегий иммунотерапии рака, включая инъекции различных иммунных эффекторных клеток, размноженных и «обученных» в клеточной культуре. В качестве альтернативы для достижения терапевтического эффекта могут быть использованы клетки, представляющие опухолевый антиген на своей поверхности в «понятном» для иммунной системы виде. Результаты исследований в этой области представлены в тысячах текстов, ручной анализ которых затруднен. Мы разработали подход для автоматического анализа текстов в этой области биомедицинской науки. В данной работе мы представляем первые результаты автоматического анализа данных, извлеченных из абстрактов научных статей, доступных в PubMed. На корпусе извлеченных текстов мы демонстрируем ассоциации между типами опухолей и наиболее часто используемыми способами клеточной терапии.

This paper presents a multifunctional support system for evidence-based medical technology that permits medical publications and reference information to be searched for in external information resources, makes it possible to formulate questions in medically accepted formats and to perform a critical analysis of publications according to the principles of evidence-based medicine, enables clinical and statistical calculators to be used, and permits answers to clinical questions to be automatically formed and archived. The system has been tested at a multi-profile hospital and demonstrated efficiency in clinical decision-making support.

The use of computer information and Internet technologies to solve clinical problems becomes trivial but is still a topical and promising direction closely related to the «Healthcare 2.0» concept. The methodology for developing virtual patient consultation systems is associated with the capabilities to process obtained data and optimize the choice of optimal tactics for further treatment, taking opportunities of the Internet. The study objective was to develop an automatic virtual consultation system for patients with spine and spinal cord diseases on the basis of the online vertebrologi.ru portal.33 signs with different answer variants were selected for advising. On the basis of these signs, an expert advised more than 170 patients.On the basis of consultation analysis, a set of rules was developed to automatically generate a consultation text, depending on selected sign values. Currently, the automatic virtual consultation system, which was developed jointly by the Institute for Systems Analysis, Burdenko Neurosurgical Institute, and Volynskaya Clinical Hospital №1, operates online on the vertebrologi.ru portal as one of its modules.Привлечение информационных компьютерных и интернет-технологий для решения клинических задач перестает быть новым, но по-прежнему остается актуальным и перспективным направлением, тесно связанным с концепцией 'Медицина 2.0'. Методология разработки виртуальных консультативных систем для пациентов связана с возможностями обрабатывать полученные данные и оптимизировать выбор оптимальной тактики дальнейшего лечения, используя возможности сети Интернет. Цель исследования - разработать автоматическую виртуальную консультацию для пациентов с заболеваниями позвоночника и спинного мозга на базе портала vertebrologi.ru, функционирующую в режиме online. Материал и методы. Для проведения консультации было отобрано 33 признака с разными вариантами ответов. На основании этих признаков экспертом было проконсультировано более 170 пациентов. Результаты и заключение. Путем анализа консультаций была разработана база правил, позволяющих автоматически сформировать текст консультации в зависимости от выбранных значений признаков. В настоящий момент система автоматической виртуальной консультации, разработанная совместно специалистами Института системного анализа РАН, НИИ нейрохирургии им. акад. Н.Н. Бурденко и Клинической больницы #1 ('Волынская'), функционирует в режиме online на портале vertebrologi.ru в качестве одного из его модулей.

The increase in the prevalence of allergic diseases requires the active implementation of the capabilities of modern technology in the process of providing health care for children with asthma. The paper describes the system for remote monitoring for asthma patients receiving treatment atthe Scientific Center of Children Health. We describe the method of estimating of the state of patients, the method of selection of medication therapy, the knowledge base and the rules implemented in it, interaction with the mobile application. The use of the system is necessary to improve disease control and prevention of exacerbations and improve quality of life as a sick child and his family.

Review is dedicated to the problem of remote monitoring of health status. Existing approaches to the organization of an outdoor monitoring of a patient using telemedicine technologies are reviewed and analyzed. A new approach to risk management of a patient which meets the requirements of pediatric hospital is provided.

To assess the results of use of lumbar spine on-line registry in 2012 (IV quarter).The Burdenko Neurosurgery Institute of the Russian Academy of Medical Sciences (RAMS) and the System Analysis Institute of the Russian Academy of Sciences (RAS) have developed an electronic "on-line" portal of the Spine Registry for Degenerative Lumbar Spine Diseases. The data on 1295 retrospective and 145 prospective patients who underwent treatment in Burdenko Neurosurgery Institute, the "AXIS" clinic, Medical Centre of the Bank of Russia, "Marina Spine Clinic" LA, USA and in the Neurosurgery department of Research Institute of Traumatology and Orthopedics, Nizhny Novgorod were analyzed. Since May 2012 to the present time outcomes of 1295 (retrospective group) and 145 (prospective group) patients with Degenerative Lumbar Spine Diseases, which underwent treatment from 2002 to 2012 were entered into online registry and subsequently analyzed. The current study has revealed two problems that need to be discussed. First problem is that the archived information is not sufficient for data base update. The second problem is low activity of many physicians in inputting data into the register. We believe that the solution of these problems lies in the plane of synchronization of on-line registry with electronic medical records. This synchronization between registry and online records will allow studying their joint work. If found to be successful after the development of the other sections of the register they will be added to an already running version as provided by the principles of its work - scalability and extesibility. The results of this work will be profile of vertebrological version of electronic medical records. In the future it could be used in clinics dealing with spine disorders.Since May 2012 the outcomes of 1295 (retrospective group) and 145 (prospective group) patients with Degenerative Lumbar Spine Diseases, who were operated on in Burdenko Neurosurgery Institute (Moscow, Russia), minimally invasive spinal surgery clinic "AXIS" (Moscow, Russia), Medical Centre of The Bank of Russia (Moscow, Russia), "Marina Spine Clinic" (LA, USA) and Neurosurgery department of Research Institute of Traumatology and Orthopedics (Nizhny Novgorod, Russia) from 2002 to 2012 were analyzed. The perspective of this work is development of other parts of spine registry (for cervical and thoracic spine) and improving the outcome assessment process in Russian spinal surgery clinics.

The aim of the study was to develop a virtual clinical diagnostic support system of degenerative lumbar spinal stenosis on database of spine registry.Choice of criteria's for diagnostic system was made on symptom analysis of 298 patients with lumbar spinal stenosis. Also was analysed a group of patient with disc herniation's for sensitivity and specify assessment of developed diagnostic support system. Represented clinical diagnostic support system allows identifying patients with degenerative lumbar spinal stenosis on stage of patient's primary visit. System sensitivity and specify are 90 and 71% respectively."Online" mode of diagnostic system in structure of spine registry provides maximal availability for specialists, regardless of their locations. Development of tools "medicine 2.0" is the actual direction for carrying out further researches with which carrying out the centralized baea collection by means of specialized registers helps.