Decision Support Systems In Medicine Research Articles

МЕТОДОЛОГИЯ СОЗДАНИЯ ИНДИВИДУАЛЬНОЙ МОДЕЛИ ЗДОРОВЬЯ ЧЕЛОВЕКА С ИСПОЛЬЗОВАНИЕМ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА

Background. In the modern world, the role of artificial intelligence in healthcare is becoming increasingly significant, providing new opportunities to transform traditional methods of diagnosis, treatment and medical data management. This technological breakthrough not only improves the efficiency of medical procedures, but also opens up new per-spectives in the prevention and treatment of diseases. The aim. To emphasize the need to create a personalized health model using artificial intelligence to help an individual achieve and maintain optimal health and well-being. In the field of personalized treatment, artificial intelligence plays an important role, taking into account the unique characteristics of each patient. Algorithms analyze genetic information, medical history and responses to previous therapies to develop optimal treatment plans. This opens the way to individualized medicine, where the approach to each patient is based on his or her unique characteristics. Despite all the positive aspects, the introduction of artificial intelligence in healthcare also raises questions of data privacy, ethical issues and technology security. However, if these issues are resolved, artificial intelligence promises to significantly improve the quality and accessibility of medical treatment, opening new horizons in healthcare. Results. In this article, we describe breakthroughs in artificial intelligence technologies and biomedical applications, identify problems of using and further development in medical artificial intelligence systems and summarize the economic, legal and social consequences of using artificial intelligence in healthcare, and propose a scheme for constructing a model of individual human health using artificial intelligence. Conclusion. The results of the analysis of modern scientific literature allow us to draw a conclusion about the potential for creating more effective and personalized approaches to the problem of individual health using integrated artificial intelligence technologies. The proposed methodology can serve as the basis for the development of innovative deci-sion support systems in medicine and improving the quality of medical care.

Acceptance of Artificial Intelligence and Decision Support Systems in Medicine (PubMed).

Abstract The objective of this study is to identify factors affecting acceptance of AI and decision support systems (DSS) in medicine and give an indication of relevance: What themes influence the acceptance of AI and DSS in medicine? How can those themes be structured based on their content? Which aspects are most important? The hypothesis is that the acceptance of AI and DSS in medicine is influenced by various factors including clinical and task-specific aspects, technical, data- & knowledge and user-training aspects, administrative aspects, patient & health aspects and social aspects. The importance of factors is unknown.

DEVELOPMENT OF THE STRUCTURE OF AN AUTOMATED SYSTEM FOR DIAGNOSING DISEASES

Today, the importance of information support for various medical technologies has increased significantly. The use of modern information technologies is becoming a critical factor in the development of most branches of knowledge and areas of practice, so the development and implementation of information systems is an urgent task. The clinical decision support system provides clinicians and stakeholders with individualized patient assessments and recommendations to assist in the clinical decision-making process.
 Knowledge-based information systems are widely used in medicine around the world. Modern technical capabilities make it possible to reach a qualitatively new level of presentation of the course of the disease, namely, based on appropriate mathematical models, to model the typical development of the pathological process in a particular disease, to speed up the process of diagnosing and receiving recommendations on treatment protocols.
 In Kazakhstan, there is no variety of decision support systems in medicine, especially in the process of diagnosing diseases. The purpose of this study is to develop the structure of an automated system for diagnosing diseases. The Unified Modeling Language (UML) is used as a design tool. The structure of the automated system is presented; the main components and key terms are considered. A mathematical model of diagnostics is shown (in the example of diseases of intestinal and pancreatic insufficiency) based on decision-making methods with fuzzy initial data. A diagram of the data flows of the system is presented. Thus, the paper proposes the structure of an automated system that will contribute to high-quality diagnostics due to an effective method of system organization and the use of fuzzy set theory methods.

NOISE REDUCTION OF LUNGS COMPUTED TOMOGRAPHY IMAGES

This article deals with the problem of improving the quality of the decision support system by improving the preprocessing of human lungs computed tomography images. These images are low-contrast and contain various noise, so pre-processing such images is an actual current issue. This problem is very important because quality of the preprocessing determines the efficiency of the decision support system. decision support system is used in the processing of computerized tomograms to reduce the level of noise in images in order to increase the probability of correct diagnosis. The aim of the work was to create a better method for apply in this applied area. This article has been analyzed existing methods of reducing the level of noise in the images of humans lungs computed tomography. This work revealed the main disadvantages of the existing methods. Combined method with contrasting was proposed for reducing the level of noise on images of computed tomography, on the basis of the received information. The efficiency of the proposed method was evaluated by three indicators and compared with the efficiency of the considered methods. Based on the data of these assessments, the method with contrasting turned out to be the best among those considered, which shows the expediency of applying the proposed method in real decision support systems in medicine. The application of the combined method with contrasting which proposed in the article for the preprocessing of images of humans lungs computed tomography will improve the quality of the system as a whole, which in turn will increase the probability of correct diagnosis..

The effect of imputing missing clinical attribute values on training lung cancer survival prediction model performance.

According to the estimations of the World Health Organization and the International Agency for Research in Cancer, lung cancer is the most common cause of death from cancer worldwide. The last few years have witnessed a rise in the attention given to the use of clinical decision support systems in medicine generally and in cancer in particular. These can predict patients' likelihood of survival based on analysis of and learning from previously treated patients. The datasets that are mined for developing clinical decision support functionality are often incomplete, which adversely impacts the quality of the models developed and the decision support offered. Imputing missing data using a statistical analysis approach is a common method to addressing the missing data problem. This work investigates the effect of imputation methods for missing data in preparing a training dataset for a Non-Small Cell Lung Cancer survival prediction model using several machine learning algorithms. The investigation includes an assessment of the effect of imputation algorithm error on performance prediction and also a comparison between using a smaller complete real dataset or a larger dataset with imputed data. Our results show that even when the proportion of records with some missing data is very high (>80%) imputation can lead to prediction models with an AUC (0.68-0.72) comparable to those trained with complete data records.

Learning a Health Knowledge Graph from Electronic Medical Records

Demand for clinical decision support systems in medicine and self-diagnostic symptom checkers has substantially increased in recent years. Existing platforms rely on knowledge bases manually compiled through a labor-intensive process or automatically derived using simple pairwise statistics. This study explored an automated process to learn high quality knowledge bases linking diseases and symptoms directly from electronic medical records. Medical concepts were extracted from 273,174 de-identified patient records and maximum likelihood estimation of three probabilistic models was used to automatically construct knowledge graphs: logistic regression, naive Bayes classifier and a Bayesian network using noisy OR gates. A graph of disease-symptom relationships was elicited from the learned parameters and the constructed knowledge graphs were evaluated and validated, with permission, against Google’s manually-constructed knowledge graph and against expert physician opinions. Our study shows that direct and automated construction of high quality health knowledge graphs from medical records using rudimentary concept extraction is feasible. The noisy OR model produces a high quality knowledge graph reaching precision of 0.85 for a recall of 0.6 in the clinical evaluation. Noisy OR significantly outperforms all tested models across evaluation frameworks (p < 0.01).

A New Approach of Dynamic Fuzzy Cognitive Knowledge Networks in Modelling Diagnosing Process of Meniscus Injury * *The present work was financially supported by the "Andreas Mentzelopoulos Scholarships University of Patras".

Abstract A new approach of Dynamic Fuzzy Cognitive Knowledge Networks is presented. This is an evolutionary type of Fuzzy Cognitive Maps (FCM) that arose from the need for updating classic methodology in order to overcome its drawbacks, concerning the single calculation rule, stability and real time problems and expand its use in a variety of applications. This new approach is being tested for its accuracy in Decision Support Systems in medicine, trying to model knee injuries by using 17 real cases of patients. The new proposed model is able to diagnose meniscus injuries and to distinguish between acute and degenerative injury. Subsequently we observe the evolution of the injury by administering a proposed treatment by the physician. Results of this new method, which are presented in detail, are very satisfactory for both two levels and treatment stage, and in total agreement with Magnetic Resonance Imaging outcomes. The whole methodology is the outcome of a close collaboration between engineers and medical doctors and is significant because it is a promising tool which sets aside the main disadvantages of Fuzzy Cognitive Maps and allows us a wide use in many real time problems.

Use of Fuzzy Logic Based Decision Support Systems in Medicine

The complexity of the problems that are faced within people’s decision-making process can reveal a variety of challenges in the solution process. The increasing complexity of the events faced, make...

Research of feasibility of using decision support systems in medicine. analysis of disadvantages and a way to their elimination

This article examines the feasibility of using decision support systems in modern medicine, literature review of presented problems and loss statistics is conducted. The main approaches, tools and principles of development of intelligent systems are also outlined; existing DSS are analyzed, their disadvantages are highlighted and solutions to elimination of common mistakes during development of decision support systems are given

Decision Support for Mental Health

Decision support systems represent an important tool offering assistance with the decision making process in a variety of applications. This paper starts with recalling the basic principles and structure of decision support systems in medicine from a general perspective. Their effect in terms of both potential and limitations for finding the diagnosis, prognosis and therapy are overviewed from the points of view of health care effectiveness and patient safety. The authors are particularly interested in the specialty field of psychiatry. They discuss its specific challenges and analyze the slower penetration of telemedicine tools to psychiatry compared to other clinical fields. Finally, they claim that the development of decision support systems play a key role in the development of the concept of information-based medicine in general as well as to the particular context of information-based psychiatry.

A Framework Using Computational Intelligence Techniques for Decision Support Systems in Medicine

The expressive growth in number of healthcare organizations has brought a great pressure on the Hospital Information Management. The increase in the number of patients imposes an extra burden to IT services of hospitals, whose information has to be used for administrative purposes but also has to be provided as actual aid for doctors. At the epicenter of this demand, Support Systems for Clinical Diagnostics, a kind of Decision Support Systems are especially designed to assist medical professionals in the exercise of the diagnosis itself. The two factors mentioned above (increase in the number of patients and growth in number of healthcare organizations) also demand the need for new and better knowledge management tools for these organizations. In line with this necessity, i.e. to build specialized applications of great efficacy in the shortest possible time, indicates that the design of a framework appears to be mandatory. A framework can be defined as an abstract design and implementation for application development on a particular problem domain. Here we provide the first ideas in the construction of an object-oriented framework for Clinical Diagnostics Supporting Systems development. Moreover we describe the first instance of the framework proposed, also the architecture and its principles of use.

Designing for Explanation in Health Care Applications of Expert Systems

A great deal has been written about the role of clinical decision support systems in medicine in recent years—an important category of which are expert systems. Expert systems would normally contain an explanation module—the subject of a great deal of research interest in the 1980s when the main problem-solving task for medical expert systems was diagnostics. However, expert systems nowadays are more likely to perform tasks other than diagnosis, yet the role of explanation in expert systems has been largely ignored in the health care literature since this time. Furthermore, user requirements can vary considerably in the health care domain and may include physicians, medical researchers, administrators, and patients. Such user groups would have differing levels of knowledge and goals, which would impact on the type of explanatory support provided by the system. This article examines the potential benefits of explanation facilities for a range of clinical tasks and also considers the ways in which explanation facilities may be delivered so as to be of benefit to these categories of health care user for these tasks.

Computer‐Aided Diagnosis in Breast Magnetic Resonance Imaging

In this paper, we review the role played by breast magnetic resonance imaging in the detection and diagnosis of breast cancer. This is followed by a discussion of clinical decision support systems in medicine and their contributions in breast magnetic resonance imaging interpretation. We conclude by discussing the future of computer-aided diagnosis in breast magnetic resonance imaging.

Special issue: Advances in medical intelligent decision support systems

This special issue is concerned with Advances in Medical Decision Support Systems. Computer science contributions to the biomedical community started with the first expert systems. Since then advances have taken place in both the medical and biological domains and also in computer science technology. This means that additional automated support can be made available to these types of applications. These advances and their contribution to the biomedical community are the focus of this special issue. The application of Intelligent Decision Support Systems in medicine is the subject of considerable ongoing research, which concentrates on modelling some of the human actions or thinking processes, recognising diseases from a variety of input sources and even extends to the representation and querying of gene data from microarray databases. Five papers have been selected, after rigorous review, for this special issue. These cover two categories: the first set of papers focuses on the medical domain and mainly on classification of medical data for accurate diagnoses. The second set of papers focuses on biological data and more importantly retrieval queries from microarray data and relationships between gene products. Computational intelligent methodologies are used for the detection of breast cancer in the paper by Geraldo Braz Junior et al. Breast cancer is the major cause

AN EVALUATION OF THE USEFULNESS OF EXPLANATION IN A CASE‐BASED REASONING SYSTEM FOR DECISION SUPPORT IN BRONCHIOLITIS TREATMENT

The research presented here explores the hypothesis that the deployment and acceptance of decision support systems in medicine will be enhanced if the basis for the recommendation produced by the system is apparent. We describe a decision support system for advising on patients suffering from bronchiolitis. This system supports its recommendations with precedent cases selected to support the recommendation along with justification text that highlights aspects of these cases relevant to the query case. It also presents an estimate of its confidence in the recommendation. The main contribution of this paper is an evaluation of this system in a clinical context. The evaluation shows that this type of explanation does enhance the usefulness of the system for practitioners.

Prediction of Cardiovascular Risk in Hemodialysis Patients by Data Mining

The objective of this work was to contribute to the development, validation and application of data mining methods for prediction in decision support systems in medicine. The particular focus was on the prediction of cardiovascular risk factors in hemodialysis patients, specifically the interventricular septum (IVS) thickness of the heart of individual patients as an important quantitative indicator to diagnose left ventricular hypertrophy. The work was based on data from 63 long-term hemodialysis patients of the KfH Dialysis Centre in Jena, Germany. The approach applied is based on data mining methods and involves four major steps: data based clustering, cluster based rule extraction, rulebase construction and cluster and rule based prediction. The methods employed include crisp and fuzzy algorithms. At each step, logical and medical validation of results was carried out. Different sets of randomly selected patient data were used to train, test and optimize the clusterbases and rulebases for prediction. Using the best clusterbase/rulebase combination designed, the IVS thickness cluster ('small' or 'large') was predicted correctly for 30 of the 35 patients with known IVS values in the training data set; no patient was predicted incorrectly and 5 were parity predicted. For the test data set, 4 of the 6 patients with known IVS values were predicted correctly, no patient incorrectly and 2 parity. These results did not substantially differ from those obtained using the second best clusterbase/rulebase combination which was finally recommended for use based on further performance criteria. The prediction of the IVS thickness clusters of the 22 patients with unknown IVS values also yielded good results that were (and could only be) validated by a medical individual risk assessment of these patients. The approach applied proved successful for the cluster and rule based prediction of a quantitative variable, such as IVS thickness, for individual patients from other variables relevant to the problem. The results obtained demonstrate the high potential of the approach and the methods developed and validated to support decision-making in hemodialysis and other fields of medicine by individual risk prediction.

Explaining the output of ensembles in medical decision support on a case by case basis

The use of ensembles in machine learning (ML) has had a considerable impact in increasing the accuracy and stability of predictors. This increase in accuracy has come at the cost of comprehensibility as, by definition, an ensemble model is considerably more complex than its component models. This is of significance for decision support systems in medicine because of the reluctance to use models that are essentially black boxes. Work on making ensembles comprehensible has so far focused on global models that mirror the behaviour of the ensemble as closely as possible. With such global models there is a clear tradeoff between comprehensibility and fidelity. In this paper, we pursue another tack, looking at local comprehensibility where the output of the ensemble is explained on a case-by-case basis. We argue that this meets the requirements of medical decision support systems. The approach presented here identifies the ensemble members that best fit the case in question and presents the behaviour of these in explanation.

Database and Knowledge Base Integration – A Data Mapping Method for Arden Syntax Knowledge Modules

One of the most important categories of decision-support systems in medicine are data driven systems where the inference engine is linked to a database. It is, therefore, important to find methods that facilitate the implementation of database queries referred to in the knowledge modules. A method is described for linking clinical databases to a knowledge base with Arden Syntax modules. The method is based on a query meta-database including templates for SQL queries which is maintained by a database administrator. During knowledge module authoring the medical expert refers only to a code in the query meta-database; no knowledge is needed about the database model or the naming of attributes and relations. The method uses standard tools, such as C+2 and ODBC, which makes it possible to implement the method at many platforms and to link to different clinical databases in a standardized way.

The role of decision-support systems in medicine.

The role of decision-support systems in medicine.

Evaluation of decision support systems in medicine

Evaluation deals with the measurement or judgement of system characteristics and with comparison of these with the frame of reference. Evaluation of medical decision support systems is important because these systems are planned to support human decision making in tasks where information from different sources is combined to support clinicians' decisions concerning diagnosis, therapy planning and monitoring of the disease and treatment processes. As the field of decision support systems is still relatively unexplored, standards or generally accepted methodologies are not yet available for evaluation. Evaluation of medical decision support systems should be approached from the perspectives of knowledge acquisition, system development life-cycle and user-system integrated environment.