Case-based medical informatics

BACKGROUND: The "applied" nature distinguishes applied sciences from theoretical sciences. To emphasize this distinction, we begin with a general, meta-level overview of the scientific endeavor. We introduce the knowledge spectrum and four interconnected modalities of knowledge. In addition to the traditional differentiation between implicit and explicit knowledge we outline the concepts of general and individual knowledge. We connect general knowledge with the "frame problem," a fundamental issue of artificial intelligence, and individual knowledge with another important paradigm of artificial intelligence, case-based reasoning, a method of individual knowledge processing that aims at solving new problems based on the solutions to similar past problems. We outline the fundamental differences between Medical Informatics and theoretical sciences and propose that Medical Informatics research should advance individual knowledge processing (case-based reasoning) and that natural language processing research is an important step towards this goal that may have ethical implications for patient-centered health medicine. DISCUSSION: We focus on fundamental aspects of decision-making, which connect human expertise with individual knowledge processing. We continue with a knowledge spectrum perspective on biomedical knowledge and conclude that case-based reasoning is the paradigm that can advance towards personalized healthcare and that can enable the education of patients and providers. We center the discussion on formal methods of knowledge representation around the frame problem. We propose a context-dependent view on the notion of "meaning" and advocate the need for case-based reasoning research and natural language processing. In the context of memory based knowledge processing, pattern recognition, comparison and analogy-making, we conclude that while humans seem to naturally support the case-based reasoning paradigm (memory of past experiences of problem-solving and powerful case matching mechanisms), technical solutions are challenging. Finally, we discuss the major challenges for a technical solution: case record comprehensiveness, organization of information on similarity principles, development of pattern recognition and solving ethical issues. SUMMARY: Medical Informatics is an applied science that should be committed to advancing patient-centered medicine through individual knowledge processing. Case-based reasoning is the technical solution that enables a continuous individual knowledge processing and could be applied providing that challenges and ethical issues arising are addressed appropriately

Case Retrieval using Bhattacharya Coefficient with Particle Swarm Optimization

Now a day, health information management and utilization is the demanding task to health informaticians for delivering the eminence healthcare services. Extracting the similar cases from the case database can aid the doctors to recognize the same kind of patients and their treatment details. Accordingly, this paper introduces the method called H-BCF for retrieving the similar cases from the case database. Initially, the patient’s case database is constructed with details of different patients and their treatment details. If the new patient comes for treatment, then the doctor collects the information about that patient and sends the query to the H-BCF. The H-BCF system matches the input query with the patient’s case database and retrieves the similar cases. Here, the PSO algorithm is used with the BCF for retrieving the most similar cases from the patient’s case database. Finally, the Doctor gives treatment to the new patient based on the retrieved cases. The performance of the proposed method is analyzed with the existing methods, such as PESM, FBSO-neural network, and Hybrid model for the performance measures accuracy and F-Measure. The experimental results show that the proposed method attains the higher accuracy of 99.5% and the maximum F-Measure of 99% when compared to the existing methods

Towards case-based medical learning in radiological decision making using content-based image retrieval

<p>Abstract</p> <p>Background</p> <p>Radiologists' training is based on intensive practice and can be improved with the use of diagnostic training systems. However, existing systems typically require laboriously prepared training cases and lack integration into the clinical environment with a proper learning scenario. Consequently, diagnostic training systems advancing decision-making skills are not well established in radiological education.</p> <p>Methods</p> <p>We investigated didactic concepts and appraised methods appropriate to the radiology domain, as follows: (i) Adult learning theories stress the importance of work-related practice gained in a team of problem-solvers; (ii) Case-based reasoning (CBR) parallels the human problem-solving process; (iii) Content-based image retrieval (CBIR) can be useful for computer-aided diagnosis (CAD). To overcome the known drawbacks of existing learning systems, we developed the concept of image-based case retrieval for radiological education (IBCR-RE). The IBCR-RE diagnostic training is embedded into a didactic framework based on the Seven Jump approach, which is well established in problem-based learning (PBL). In order to provide a learning environment that is as similar as possible to radiological practice, we have analysed the radiological workflow and environment.</p> <p>Results</p> <p>We mapped the IBCR-RE diagnostic training approach into the Image Retrieval in Medical Applications (IRMA) framework, resulting in the proposed concept of the IRMAdiag training application. IRMAdiag makes use of the modular structure of IRMA and comprises (i) the IRMA core, i.e., the IRMA CBIR engine; and (ii) the IRMAcon viewer. We propose embedding IRMAdiag into hospital information technology (IT) infrastructure using the standard protocols Digital Imaging and Communications in Medicine (DICOM) and Health Level Seven (HL7). Furthermore, we present a case description and a scheme of planned evaluations to comprehensively assess the system.</p> <p>Conclusions</p> <p>The IBCR-RE paradigm incorporates a novel combination of essential aspects of diagnostic learning in radiology: (i) Provision of work-relevant experiences in a training environment integrated into the radiologist's working context; (ii) Up-to-date training cases that do not require cumbersome preparation because they are provided by routinely generated electronic medical records; (iii) Support of the way adults learn while remaining suitable for the patient- and problem-oriented nature of medicine. Future work will address unanswered questions to complete the implementation of the IRMAdiag trainer.</p

The Purola model and the Holistic Concept of Man metaphor as bases for the networked view of decision-making in eHealth and eWelfare

The essence of the essay is to outline two models or metaphors of different philosophic and practical origins, with the aim of recombining the two to encompass some relevant aspects of modern information systems contexts in eHealth and eWelfare. The Purola model and the Holistic Concept of Man refer to the same real world phenomena but with different ontological and epistemological views. However, many further applications in areas of education, rehabilitation, accounting research, computerized decision support, sociomedical and information systems research remain yet to be contrived. Deeper analyzes of decision making environments of networked systemic wholes in eHealth and eWelfare are needed, both on micro and macro levels. Researchers and practicioners should strive for a balance between ethical demands of individual biopsychosociality and confrontations of technological and economic efficiency, also with macro scale cooperative options in mind

Configuration interactive et contraintes : connaissances, filtrage et extensions

The value of our research work is rooted in the following observations :-1- the life cycle of products, systems, services and processes is tending to get shorter ; -2- new designs and updates of products on the market are becoming more and more frequent, leading to increasingly short design cycles ; -3 technologies are constantly changing, requiring permanent, ongoing acquisition of knowledge ; -4-the diversity of products offered on the market is growing all the time, ranging from customizable or configurable to made-to-measure or designed to order.These trends, and the mass of information and knowledge that requires treating as a result of them, are placing heavy demands on designers, requiring ever more attentiveness and increasingly intense cognitive effort. The result is an increased risk that the product does not fully meet the customer’s needs, that it is difficult to implement or manufacture, or that it will be prohibitively expensive. The aim of our work is thus to help the design process to reduce these risks and errors by delivering software tools and methodological environments that serve to capitalize and exploit general, contextual, academic, expert or business knowledge.Our work on various complex industrial cases has led us to take into consideration two kinds of knowledge, involving on the one hand the "product domain" and on the other the "product diversity element". Each kind of knowledge leads to differing industrial cases. The first kind of knowledge encompasses the scientific and technical aspects, but also the specific rules governing the business in question. This knowledge is required in order to define the product itself, and involves issues that can be resolved by aiding the product /system/service design. The second kind of knowledge relates to the diverse nature of the products, and involves issues of customization or configuration of the product/system/service.Our aim is to help in what might be called "routine" design, where different kinds and various types of knowledge exist, due to the recurrent nature of the activity. We consider that aid in design or configuration can be formalized, either completely or partially, in the form of a constraint satisfaction problem (CSP). In this context, we focus more specifically on interactive decision-support, by introducing the principles of filtering or constraint propagation. The diversity of knowledge formalized as a CSP and the interaction with the user allow us to assemble and adapt filtering algorithms in a generic constraint propagation engine, integrated in our CoFiADe software solution.In addition, this formalism based on CSP constraints is complemented by : - ontologies to structure knowledge and facilitate its reuse throughout the development cycle, - analogy-based approaches taking advantage of contextual knowledge encapsulated in the case under study, so as to make recommendations to the user on the choice of values, - evolutionary approaches to optimize the search for multi-criteria solutions.Les travaux de recherche présentés dans ce mémoire trouvent leurs fondements dans les constats suivants :-1- la durée de vie des produits et systèmes tend à se réduire,-2- les conceptions et les actualisations des produits mis sur le marché sont de plus en plus fréquentes alors que les cycles de conception sont toujours plus brefs,-3- les technologies employées en constante évolution nécessitent une acquisition de connaissance permanente,-4- la diversité des produits offerte sur les marchés ne cesse de croître allant des produits personnali- sables ou configurés jusqu’aux produits sur-mesure et conçus à la commande.Ces tendances et la masse d’informations et de connaissances à traiter en découlant exigent des concepteurs toujours plus d’attention et un travail cognitif toujours plus intense. Il en résulte une augmentation des risques, que le produit réponde imparfaitement aux besoins du demandeur, qu’il soit difficilement réalisable et fabricable, ou encore qu’il le soit à un coût prohibitif. L’objectif de nos travaux est donc de limiter ces risques et erreurs en proposant des outils logiciels et des environnements méthodologiques destinés à capitaliser et exploiter des connaissances générales, contextuelles, académiques, expertes ou métier pour aider la conception.Les travaux effectués sur différentes problématiques industrielles ont conduit à prendre en considération deux natures de connaissances relevant du « domaine produit » et de la « diversité produit » conduisant à des problématiques industrielles différentes : la première nature de connaissance recouvre aussi bien des aspects scientifiques et techniques que des règles métier, elle est nécessaire pour la définition du produit et débouche sur des problématiques d’aide à la conception de produit ; la seconde nature est une connaissance liée à la diversité des produits, qui débouche sur les problématiques d’aide à la personnalisation ou configuration de produit.Nous visons à aider un type de conception plutôt « routinier » où de la connaissance de différentes natures et de divers types existe du fait de la récurrence de l’activité. Nous considérons de plus dans nos travaux que l’aide à la conception ou configuration peut se formaliser, complètement ou partiellement, comme un problème de satisfaction de contraintes (CSP). Dans ce cadre, nous nous intéressons plus spécifiquement à l’aide à la décision interactive exploitant les principes de filtrage ou de propagation de contraintes. Notre objectif se décline alors en l’accompagnement des concepteurs dans la construction des solutions répondant au mieux à leurs problèmes, en retirant progressivement de l’espace des solutions, celles qui ne sont plus cohérentes avec les décisions prises, en estimant celles-ci au fil de leur construction et/ou en les optimisant.en complément, nous associons à ce formalisme à base de contraintes CSP :- des ontologies pour structurer les connaissances et faciliter leur réutilisateion sur l’ensemble du cycle de développement,- des approches par analogie exploitant de la connaissance contextuelle encapsulée dans des cas afin de proposer à l’utilisateur des recommandations quant aux choix de valeurs,- des approches évolutionnaires pour optimiser la recherche des solutions de manière multicritère