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Search Engine Decision-Relevant Information and Exchange with the Information System
Abstract
Modern decision support systems (DSS) should not work autonomously, but be embedded in information systems (particularly in healthcare, in electronic health records). This will allow you to extract the necessary data in automatic mode and then supplement them in a dialogue with the user. In dynamic systems, when monitoring certain indicators, it is necessary to process the data flow in real time, taking into account the boundary conditions. This will allow providing situational decision-making control, which is especially important in emergency conditions. In addition, there is a need to feed back the formed hypotheses to the information system and explain the proposed solutions. At the same time, information that was obtained additionally in a dialogue with the user of the intelligent system must be transmitted to the database and recorded in certain fields.
The purpose of this study is to investigate organizational drivers of green innovation and green development in China's manufacturing industry. The study presents a theoretical framework that consists of technology compatibility, institutional pressure, and green transformation. These factors impact organizations' green innovation adoption behavior, improving enterprise green development. Analysis of 321 executives and non-executives’ data using Partial Least Squares Structural Equation Modeling. Technology compatibility aligns enterprises with new systems and mitigates integration challenges, institutional pressures (mimetic, coercive, and normative) motivate adherence to green development strategies, and green transformation promotes sustainability across production, ecology, and carbon neutrality. This shifts organizational behavior toward green innovation adoption for greater development. The study also finds partial mediation effects between exogenous and endogenous components, supporting theory and practice.
Despite the current slowdown in global economic growth due to the impact of COVID-19, the digital economy is still performing well. Under the background of double carbon, green innovation and intelligent production of manufacturing enterprises have become the general trend of sustainable development. It is particularly important to study the integration of digital technology into green innovation and production processes to improve the performance of digital green innovation and the competitiveness of enterprises. However, the integration of digital technology and green innovation from the perspective of knowledge management has not been fully introduced into current literatures. In this study, hierarchical regression and fsQCA approaches were used to empirically verify the adoption process of digital green innovation activities and the impact of digital green knowledge creation on digital green innovation performance (DGIP), and explores the moderating effects of digital green risk perception (DGRP) and digital green complexity perception (DGCC) through 429 questionnaires from Chinese manufacturing enterprises. In addition, knowledge search is divided into three dimensions: scientific digital green knowledge search (SDGKS), market digital green knowledge search (MDGKS) and supply chain digital green knowledge search (SCDGKS). The results show that: i) SDGKS promotes exploitative digital green knowledge creation (EDGKC). MDGKS has a positive impact on both utilizing digital green knowledge creation (ADGKC) and EDGKC. SCDGKS promotes EDGKC. ii) The relationship between SDGKS and EDGKC is only moderated by DGCC (positive). The relationship between MDGKS and EDGKC is only moderated by DGCC (negative). The relationship between SCDGKS and EDGKC is moderated by DGRP (negative) and DGCC (negative). iii) There is an inverted U-shaped relationship between ADGKC and DGIP. There is a U-shaped relationship between EDGKC and DGIP. The essence of this study is to help manufacturing enterprises find external partners to improve their digital green innovation performance through external knowledge search partner selection. The conclusion of this study has certain theoretical contribution to the clarification of the complex process of digital green innovation. This study provides a theoretical basis for enterprises to select knowledge search partners according to their own environment to carry out digital green innovation activities smoothly. This study has practical value for enterprises to improve competitiveness, better survival and development process under the current environment.
Computerized clinical decision support systems, or CDSS, represent a paradigm shift in healthcare today. CDSS are used to augment clinicians in their complex decision-making processes. Since their first use in the 1980s, CDSS have seen a rapid evolution. They are now commonly administered through electronic medical records and other computerized clinical workflows, which has been facilitated by increasing global adoption of electronic medical records with advanced capabilities. Despite these advances, there remain unknowns regarding the effect CDSS have on the providers who use them, patient outcomes, and costs. There have been numerous published examples in the past decade(s) of CDSS success stories, but notable setbacks have also shown us that CDSS are not without risks. In this paper, we provide a state-of-the-art overview on the use of clinical decision support systems in medicine, including the different types, current use cases with proven efficacy, common pitfalls, and potential harms. We conclude with evidence-based recommendations for minimizing risk in CDSS design, implementation, evaluation, and maintenance.
Recently, the concept of radiomics has emerged from radiation oncology. It is a novel approach for solving the issues of precision medicine and how it can be performed, based on multimodality medical images that are non-invasive, fast and low in cost. Radiomics is the comprehensive analysis of massive numbers of medical images in order to extract a large number of phenotypic features (radiomic biomarkers) reflecting cancer traits, and it explores the associations between the features and patients' prognoses in order to improve decision-making in precision medicine. Individual patients can be stratified into subtypes based on radiomic biomarkers that contain information about cancer traits that determine the patient's prognosis. Machine-learning algorithms of AI are boosting the powers of radiomics for prediction of prognoses or factors associated with treatment strategies, such as survival time, recurrence, adverse events, and subtypes. Therefore, radiomic approaches, in combination with AI, may potentially enable practical use of precision medicine in radiation therapy by predicting outcomes and toxicity for individual patients.
The main cause of non-traumatic subarachnoid haemorrhage is an intracranial aneurysm's rupture. The choice of treatment approach is exceptionally difficult in cases of aneurysms with additional branches on the aneurysm's dome or neck. The impact of the arterial branches on local hemodynamics is still unclear and controversial question. At the same time, up-to-date methods of image processing and mathematical modeling provide a way to investigate the hemodynamic environment of aneurysms. The paper discusses hemodynamic aspects of aneurysms harboring arterial branch through the use of patient-specific 3D models and computational fluid dynamics (CFD) methods. The analysis showed that the presence of the arterial branches has a great influence on flow streamlines and wall shear stress, particularly for side wall aneurysm.
Electronic Health Records are electronic versions of patients' healthcare records. An electronic health record gathers, creates, and stores the health record electronically. The electronic health record has been slow to be adopted by healthcare providers. The federal government has recently passed legislation requiring the use of electronic records or face monetary penalties. The electronic health record will improve clinical documentation, quality, healthcare utilization tracking, billing and coding, and make health records portable. The core components of an electronic health record include administrative functions, computerized physician order entry, lab systems, radiology systems, pharmacy systems, and clinical documentation. HL7 is the standard communication protocol technology that an electronic health record utilizes. Implementation of software, hardware, and IT networks are important for a successful electronic health record project. The benefits of an electronic health record include a gain in healthcare efficiencies, large gains in quality and safety, and lower healthcare costs for consumers. Electronic health record challenges include costly software packages, system security, patient confidentiality, and unknown future government regulations. Future technologies for electronic health records include bar coding, radio-frequency identification, and speech recognition.
The use of information and communication technologies to manage chronic diseases allows the application of integrated care pathways, and the optimization and standardization of care processes. Decision support tools can assist in the adherence to best-practice medicine in critical decision points during the execution of a care pathway.
The objectives are to design, develop, and assess a clinical decision support system (CDSS) offering a suite of services for the early detection and assessment of chronic obstructive pulmonary disease (COPD), which can be easily integrated into a healthcare providers' work-flow.
The software architecture model for the CDSS, interoperable clinical-knowledge representation, and inference engine were designed and implemented to form a base CDSS framework. The CDSS functionalities were iteratively developed through requirement-adjustment/development/validation cycles using enterprise-grade software-engineering methodologies and technologies. Within each cycle, clinical-knowledge acquisition was performed by a health-informatics engineer and a clinical-expert team.
A suite of decision-support web services for (i) COPD early detection and diagnosis, (ii) spirometry quality-control support, (iii) patient stratification, was deployed in a secured environment on-line. The CDSS diagnostic performance was assessed using a validation set of 323 cases with 90% specificity, and 96% sensitivity. Web services were integrated in existing health information system platforms.
Specialized decision support can be offered as a complementary service to existing policies of integrated care for chronic-disease management. The CDSS was able to issue recommendations that have a high degree of accuracy to support COPD case-finding. Integration into healthcare providers' work-flow can be achieved seamlessly through the use of a modular design and service-oriented architecture that connect to existing health information systems.
Seoul National University Bundang Hospital, which is the first Stage 7 hospital outside of North America, has adopted and utilized an innovative and emerging information technology system to improve the efficiency and quality of patient care. The objective of this paper is to briefly introduce the major components of the SNUBH information system and to describe our progress toward a next-generation hospital information system (HIS).
SNUBH opened in 2003 as a fully digital hospital by successfully launching a new HIS named BESTCare, "Bundang hospital Electronic System for Total Care". Subsequently, the system has been continuously improved with new applications, including close-loop medication administration (CLMA), clinical data warehouse (CDW), health information exchange (HIE), and disaster recovery (DR), which have resulted in the achievement of Stage 7 status.
The BESTCare system is an integrated system for a university hospital setting. BESTCare is mainly composed of three application domains: the core applications, an information infrastructure, and channel domains. The most critical and unique applications of the system, such as the electronic medical record (EMR), computerized physician order entry (CPOE), clinical decision support system (CDSS), CLMA, CDW, HIE, and DR applications, are described in detail.
Beyond our achievement of Stage 7 hospital status, we are currently developing a next-generation HIS with new goals of implementing infrastructure that is flexible and innovative, implementing a patient-centered system, and strengthening the IT capability to maximize the hospital value.
Developers of health care software have attributed improvements in patient care to these applications. As with any health care intervention, such claims require confirmation in clinical trials.
To review controlled trials assessing the effects of computerized clinical decision support systems (CDSSs) and to identify study characteristics predicting benefit.
We updated our earlier reviews by searching the MEDLINE, EMBASE, Cochrane Library, Inspec, and ISI databases and consulting reference lists through September 2004. Authors of 64 primary studies confirmed data or provided additional information.
We included randomized and nonrandomized controlled trials that evaluated the effect of a CDSS compared with care provided without a CDSS on practitioner performance or patient outcomes.
Teams of 2 reviewers independently abstracted data on methods, setting, CDSS and patient characteristics, and outcomes.
One hundred studies met our inclusion criteria. The number and methodologic quality of studies improved over time. The CDSS improved practitioner performance in 62 (64%) of the 97 studies assessing this outcome, including 4 (40%) of 10 diagnostic systems, 16 (76%) of 21 reminder systems, 23 (62%) of 37 disease management systems, and 19 (66%) of 29 drug-dosing or prescribing systems. Fifty-two trials assessed 1 or more patient outcomes, of which 7 trials (13%) reported improvements. Improved practitioner performance was associated with CDSSs that automatically prompted users compared with requiring users to activate the system (success in 73% of trials vs 47%; P = .02) and studies in which the authors also developed the CDSS software compared with studies in which the authors were not the developers (74% success vs 28%; respectively, P = .001).
Many CDSSs improve practitioner performance. To date, the effects on patient outcomes remain understudied and, when studied, inconsistent.
To identify features of clinical decision support systems critical for improving clinical practice.
Systematic review of randomised controlled trials.
Literature searches via Medline, CINAHL, and the Cochrane Controlled Trials Register up to 2003; and searches of reference lists of included studies and relevant reviews.
Studies had to evaluate the ability of decision support systems to improve clinical practice.
Studies were assessed for statistically and clinically significant improvement in clinical practice and for the presence of 15 decision support system features whose importance had been repeatedly suggested in the literature.
Seventy studies were included. Decision support systems significantly improved clinical practice in 68% of trials. Univariate analyses revealed that, for five of the system features, interventions possessing the feature were significantly more likely to improve clinical practice than interventions lacking the feature. Multiple logistic regression analysis identified four features as independent predictors of improved clinical practice: automatic provision of decision support as part of clinician workflow (P < 0.00001), provision of recommendations rather than just assessments (P = 0.0187), provision of decision support at the time and location of decision making (P = 0.0263), and computer based decision support (P = 0.0294). Of 32 systems possessing all four features, 30 (94%) significantly improved clinical practice. Furthermore, direct experimental justification was found for providing periodic performance feedback, sharing recommendations with patients, and requesting documentation of reasons for not following recommendations.
Several features were closely correlated with decision support systems' ability to improve patient care significantly. Clinicians and other stakeholders should implement clinical decision support systems that incorporate these features whenever feasible and appropriate.
The use of electronically stored data requires that the data is recorded in a structured and standardized manner. This has led to the development of terminological systems. The content of these medical terminological systems is subject to continuing changes as a consequence of the developments in the field of medicine.By means of a literature study criteria were obtained for the maintenance of terminological systems. The criteria are used to develop a framework for the management of the maintenance process. The current practice of terminology maintenance was explored in a survey of twenty-seven organizations that currently maintain a terminological system. The results of the survey reveal the diversity and suboptimality of the current maintenance processes. The framework for the management of the maintenance process can be used to eliminate deficiencies in this process. This framework for terminology maintenance is an important step towards standardization of the maintenance of medical terminological systems.
In many knowledge intensive applications, it is necessary to have extensive domain-specific knowledge in addition to general-purpose knowledge bases. This paper presents a methodology for discovering domain-specific concepts and relationships in an attempt to extend WordNet. The method was tested on five seed concepts selected from the financial domain: interest rate, stock market, inflation, economic growth, and employment.
Public health surveillance is the systematic, ongoing assessment of the health of a community, based on the collection, interpretation, and use of health data. Surveillance provides information necessary for public health decision making. This text presents an organized approach to planning, developing, and implementing public health surveillance systems. It has a broad scope, discussing legal and ethical issues as well as technical problems. It goes beyond the surveillance of particular conditions to the basic elements common to the application of surveillance to any type of health-related problem. The Second Edition has been updated throughout and many chapters have been expanded. Several new chapters have been added on the changing healthcare information infrastructure, quality surveillance, pharmaceutical surveillance, and the use of surveillance information in marketing and advocacy. The standard text in this field, it will continue to serve as a textbook for public health students and a desk reference for public health practioners.
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 [1]. The last few years have seen a surge in the number of accurate, fast, publicly available name entity recognition (NER) parsers [2,3]. 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.
Deep learning has rapidly advanced in various fields within the past few years and has recently gained particular attention in the radiology community. This article provides an introduction to deep learning technology and presents the stages that are entailed in the design process of deep learning radiology research. In addition, the article details the results of a survey of the application of deep learning-specifically, the application of convolutional neural networks-to radiologic imaging that was focused on the following five major system organs: chest, breast, brain, musculoskeletal system, and abdomen and pelvis. The survey of the studies is followed by a discussion about current challenges and future trends and their potential implications for radiology. This article may be used as a guide for radiologists planning research in the field of radiologic image analysis using convolutional neural networks.
Background and purpose:
The timely diagnosis of stroke at the initial examination is extremely important given the disease morbidity and narrow time window for intervention. The goal of this study was to develop a supervised learning method to recognize acute cerebral ischemia (ACI) and differentiate that from stroke mimics in an emergency setting.
Methods:
Consecutive patients presenting to the emergency department with stroke-like symptoms, within 4.5 hours of symptoms onset, in 2 tertiary care stroke centers were randomized for inclusion in the model. We developed an artificial neural network (ANN) model. The learning algorithm was based on backpropagation. To validate the model, we used a 10-fold cross-validation method.
Results:
A total of 260 patients (equal number of stroke mimics and ACIs) were enrolled for the development and validation of our ANN model. Our analysis indicated that the average sensitivity and specificity of ANN for the diagnosis of ACI based on the 10-fold cross-validation analysis was 80.0% (95% confidence interval, 71.8-86.3) and 86.2% (95% confidence interval, 78.7-91.4), respectively. The median precision of ANN for the diagnosis of ACI was 92% (95% confidence interval, 88.7-95.3).
Conclusions:
Our results show that ANN can be an effective tool for the recognition of ACI and differentiation of ACI from stroke mimics at the initial examination.
Precision medicine is an emerging approach for treating medical disorders, which takes into account individual variability in genetic and environmental factors. Preventive or therapeutic interventions can then be directed to those who will benefit most from targeted interventions, thereby maximizing benefits and minimizing costs and complications. Precision medicine is gaining increasing recognition by clinicians, healthcare systems, pharmaceutical companies, patients, and the government. Imaging plays a critical role in precision medicine including screening, early diagnosis, guiding treatment, evaluating response to therapy, and assessing likelihood of disease recurrence. The Association of University Radiologists Radiology Research Alliance Precision Imaging Task Force convened to explore the current and future role of imaging in the era of precision medicine and summarized its finding in this article. We review the increasingly important role of imaging in various oncological and non-oncological disorders. We also highlight the challenges for radiology in the era of precision medicine.
Context:
Clinical decision support systems (CDSSs) can help clinicians assess cardiovascular disease (CVD) risk and manage CVD risk factors by providing tailored assessments and treatment recommendations based on individual patient data. The goal of this systematic review was to examine the effectiveness of CDSSs in improving screening for CVD risk factors, practices for CVD-related preventive care services such as clinical tests and prescribed treatments, and management of CVD risk factors.
Evidence acquisition:
An existing systematic review (search period, January 1975-January 2011) of CDSSs for any condition was initially identified. Studies of CDSSs that focused on CVD prevention in that review were combined with studies identified through an updated search (January 2011-October 2012). Data analysis was conducted in 2013.
Evidence synthesis:
A total of 45 studies qualified for inclusion in the review. Improvements were seen for recommended screening and other preventive care services completed by clinicians, recommended clinical tests completed by clinicians, and recommended treatments prescribed by clinicians (median increases of 3.8, 4.0, and 2.0 percentage points, respectively). Results were inconsistent for changes in CVD risk factors such as systolic and diastolic blood pressure, total and low-density lipoprotein cholesterol, and hemoglobin A1C levels.
Conclusions:
CDSSs are effective in improving clinician practices related to screening and other preventive care services, clinical tests, and treatments. However, more evidence is needed from implementation of CDSSs within the broad context of comprehensive service delivery aimed at reducing CVD risk and CVD-related morbidity and mortality.
Background:
In general, medical geneticists aim to pre-diagnose underlying syndromes based on facial features before performing cytological or molecular analyses where a genotype-phenotype interrelation is possible. However, determining correct genotype-phenotype interrelationships among many syndromes is tedious and labor-intensive, especially for extremely rare syndromes. Thus, a computer-aided system for pre-diagnosis can facilitate effective and efficient decision support, particularly when few similar cases are available, or in remote rural districts where diagnostic knowledge of syndromes is not readily available.
Methods:
The proposed methodology, visual diagnostic decision support system (visual diagnostic DSS), employs machine learning (ML) algorithms and digital image processing techniques in a hybrid approach for automated diagnosis in medical genetics. This approach uses facial features in reference images of disorders to identify visual genotype-phenotype interrelationships. Our statistical method describes facial image data as principal component features and diagnoses syndromes using these features.
Results:
The proposed system was trained using a real dataset of previously published face images of subjects with syndromes, which provided accurate diagnostic information. The method was tested using a leave-one-out cross-validation scheme with 15 different syndromes, each of comprised 5-9 cases, i.e., 92 cases in total. An accuracy rate of 83% was achieved using this automated diagnosis technique, which was statistically significant (p<0.01). Furthermore, the sensitivity and specificity values were 0.857 and 0.870, respectively.
Conclusion:
Our results show that the accurate classification of syndromes is feasible using ML techniques. Thus, a large number of syndromes with characteristic facial anomaly patterns could be diagnosed with similar diagnostic DSSs to that described in the present study, i.e., visual diagnostic DSS, thereby demonstrating the benefits of using hybrid image processing and ML-based computer-aided diagnostics for identifying facial phenotypes.
Computerized physician order entry (CPOE) has been promoted as an important component of patient safety, quality improvement, and modernization of medical practice. In practice, however, CPOE affects health care delivery in complex ways, with benefits as well as risks. Every implementation of CPOE is associated with both generally recognized and unique local factors that can facilitate or confound its rollout, and neurohospitalists will often be at the forefront of such rollouts. In this article, we review the literature on CPOE, beginning with definitions and proceeding to comparisons to the standard of care. We then proceed to discuss clinical decision support systems, negative aspects of CPOE, and cultural context of CPOE implementation. Before concluding, we follow the experiences of a Chief Medical Information Officer and neurohospitalist who rolled out a CPOE system at his own health care organization and managed the resulting workflow changes and setbacks.
Large-scale annotated corpora are a prerequisite to developing high-performance semantic role labeling systems. Unfortunately, such corpora are expensive to produce, limited in size, and may not be representative. Our work aims to reduce the annotation effort involved in creating resources for semantic role labeling via semi-supervised learning. The key idea of our approach is to find novel instances for classifier training based on their similarity to manually labeled seed instances. The underlying assumption is that sentences that are similar in their lexical material and syntactic structure are likely to share a frame semantic analysis. We formalize the detection of similar sentences and the projection of role annotations as a graph alignment problem, which we solve exactly using integer linear programming. Experimental results on semantic role labeling show that the automatic annotations produced by our method improve performance over using hand-labeled instances alone.
Clinical decision-support systems (CDSSs) comprise systems as diverse as sophisticated platforms to store and manage clinical data, tools to alert clinicians of problematic situations, or decision-making tools to assist clinicians. Irrespective of the kind of decision-support task CDSSs should be smoothly integrated within the clinical information system, interacting with other components, in particular with the electronic health record (EHR). However, despite decades of developments, most CDSSs lack interoperability features.
This chapter provides a conceptual foundation by exploring the existing literature on traditional healthcare, patient-centered healthcare, and the progression of e-health in enabling the movement towards patient-centered care. This chapter also discusses enhancing the relationship between the patient and the healthcare provider through e-health. We conclude with a discussion of the future of patient-centered e-health and future research opportunities in this area.
VisualDx is a clinical decision support tool that uses images and searchable clinical features to assist physicians in the diagnostic decision-making process. VisualDx is a database of more than 24,000 high-quality images covering a wide variety of conditions and drug eruptions. In addition, VisualDx integrates within both UpToDate and many electronic medical records and features a mobile version. This column includes a sample search and a review of the features of VisualDx.
Nowadays with the growing of the wireless connections people can access all the resources hosted in the Cloud almost everywhere. In this context, organisms can take advantage of this fact, in terms of e-Health, deploying Cloud-based solutions on e-Health services. In this paper two Cloud-based solutions for different scenarios of Electronic Health Records (EHRs) management system are proposed. We have researched articles published between the years 2005 and 2011 about the implementation of e-Health services based on the Cloud in Medline. In order to analyze the best scenario for the deployment of Cloud Computing two solutions for a large Hospital and a network of Primary Care Health centers have been studied. Economic estimation of the cost of the implementation for both scenarios has been done via the Amazon calculator tool. As a result of this analysis two solutions are suggested depending on the scenario: To deploy a Cloud solution for a large Hospital a typical Cloud solution in which are hired just the needed services has been assumed. On the other hand to work with several Primary Care Centers it's suggested the implementation of a network, which interconnects these centers with just one Cloud environment. Finally it's considered the fact of deploying a hybrid solution: in which EHRs with images will be hosted in the Hospital or Primary Care Centers and the rest of them will be migrated to the Cloud.
The National (Federal) Russian Genetic Register operates as a multifunctional system. It is designed for finding solution to the following key problems: 1. Information provision to watch families having children with hereditary and congenital diseases. 2. Follow up of the diagnostic process (including that associated with molecular-cytogenetic studies) and making predictive decisions concerning a risk of hereditary disease in the family. 3. Monitoring of new cases of congenital/hereditary disease under the effect of genotoxic and general toxic environmental factors. 4. Rendering consulting services to those concerned and reference information on the organizational/methodological problems. The relational database is composed of textual and graphical data. This enables the doctors to enter and review the pedigree in the form they have got accustomed to. Numerous built-in classification schemes help simplify and speed up the completion of the medical records. An additional information can be entered in the textual form. When requested by the user, the information about the patient and his (her) family, stored in the database, can be displayed as a summary document (a statement). To do so, a special algorithm based on the results of the logical information analysis, as well as medical history of the patients themselves and their families' members has been put forward. The document thus compiled is open for being edited by the doctor. The local computer network is operated in the "user-server" mode. For information protection purposes, an authorized access system has been devised to protect various data categories. The software tools operate in the Windows environment for IBM-compatible personal computers. Also, use is made of Microsoft SQL-Server, Visual Fox-Pro 3.0, Visual C+2 2.1, and FORTRAN 5.1. The doctor decision modules are activated with mathematical models, an expert system, and a self-learning recognizing system. The Windows NT system is a choice for the existing Federal and Regional Genetic Centers. For territorial consulting purposes other alternatives might be Windows for Workgroup 3.11 or Windows 95. The local computer networks in individual organizations will be integrated to form a Corporate Medical Genetics Service of Russia. The system is to be completed before the year 2000 in a number of phases. Up to now, program packages for family database management and recurrent risk calculations have been worked out and tested.
Practical importance of the electronic burn chart for assessment of affected body surface area among children with burn injury
- D Dolotova
- L Shurova
- L Budkevich
- B Kobrinskii
Linguistic analysis of case histories to identify risk factors for stroke
- N A Blagosklonov
- V V Donitova
- D A Kireev
- B A Kobrinsky
- I V Smirnov
Effects of computerized clinical decision support systems on practitioner performance and patient outcomes
- A X Garg
- AX Garg
EHEALTH and telemedicine: status and future steps. E-Health Telecommun
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- BA Kobrinsky
Integrated electronic medical record: state of affairs and prospects
- T V Zarubina
- TV Zarubina
Images in logical-and-linguistic artificial intelligence systems
- B A Kobrinskii
- BA Kobrinskii
Radiological images in the construction of a hybrid intelligent system
- B A Kobrinsky
- D D Dolotova
- V V Donitova
- A V Gavrilov
- BA Kobrinsky
Ehealth: a dilemma for
- D Lloyd-Williams