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The concept of medical diagnosis is reconstructed and its syntax, semantics, and pragmatics are analyzed. It is shown that medical diagnosis is relative to a complex diagnostic structure part of which are the method of diagnostic reasoning and the knowledge base used. Clinical knowledge-based systems research is interpreted as the advent of an engineering science of clinical practice.
In computational structure-based drug design, the scoring functions are the cornerstones to the success of design/discovery. Many approaches have been explored to improve their reliability and accuracy, leading to three families of scoring functions: force-field-based, knowledge-based, and empirical. The last family is the most widely used in association with docking algorithms because of its speed, even though such empirical scoring functions produce far too many false positives to be fully reliable. In this work, we describe a World Wide Web accessible database that gathers the structural information from known complexes of the PDB with experimental binding data. This database, the Ligand-Protein DataBase (LPDB), is designed to allow the selection of complexes based on various properties of receptors and ligands for the design and parametrization of new scoring functions or to assess and improve existing ones. Moreover, for each complex, a continuum of ligand positions ranging from the crystallographic position to points on the surface of the protein receptor allows an assessment of the energetic behavior of particular scoring functions.
A model to explain interpersonal trust development, and its consequences for co-operative behaviour in doctor/patient partnerships derived from the context of business relationships is applied to patient/physician relationships. Threshold barriers exist against all human behaviours or actions and trust is the process by which barriers to co-operation and compliance are overcome. Dispositional trust (a psychological trait to be trusting) is dominant in the early stages of a relationship and contributes to the weight of subsequent trust development. Co-operative behaviour or compliance ultimately requires a secure situational trust emerging from consultations, which is carried forward as learnt trust and modified in each subsequent consultation. The model comprises three types of situational trust (calculus-based, knowledge-based, and identification trust) and five co-operation criteria from which to determine an individual's tendency for co- operative behaviour. These model components can be identified and mapped from a range of qualitative data, with the aim of enhancing co-operative behaviour and efficiently achieving optimal patient compliance.
Students are most motivated and learn best when they are immersed in an environment that causes them to realize why they should learn. Perhaps nowhere is this truer than when teaching the biological sciences to engineers. Transitioning from a traditionally mathematics-based to a traditionally knowledge-based pedagogical style can challenge student learning and engagement. To address this, human pathologies were used as a problem-based context for teaching knowledge-based cell biological mechanisms. Lectures were divided into four modules. First, a disease was presented from clinical, economic, and etiological standpoints. Second, fundamental concepts of cell and molecular biology were taught that were directly relevant to that disease. Finally, we discussed the cellular and molecular basis of the disease based on these fundamental concepts, together with current clinical approaches to the disease. The basic science is thus presented within a "shrink wrap" of disease application. Evaluation of this contextual technique suggests that it is very useful in improving undergraduate student focus and motivation, and offers many advantages to the instructor as well.
RHINE is an inter-regional network in Europe having as an aim, the promotion of know-how transfer in the area of Information Technology methods and tools in the regions involved. The RHINE network's significance will be demonstrated in the health sector. Within the main scope of the project, the aims and objectives of RHINE and the participating partners encompass the extension of the nucleus network and the furtherance of Information Technology research. Application areas include distributed data base and knowledge base technologies for open regional information systems supported by other technologies for business processes.
Evidence-based clinical guidelines have been developed in an attempt to decrease practice variation and improve patient outcomes. Although a number of studies and a few commercial products have attempted to measure guideline compliance, there still exists a strong need for an automated product that can take as input large amounts of data and create systematic and detailed profiles of compliance to evidence-based guidelines. The Guideline Compliance Assessment Tool is a product presently under development in our group that will accept as input medical and pharmacy claims data and create a guideline compliance profile that assesses provider practice patterns as compared to evidence-based standards. The system components include an episode of care grouper to standardize classifications of illnesses, an evidence-based guideline knowledge base that potentially contains information on several hundred distinct conditions, a guideline compliance scoring system that emphasizes systematic guideline variance rather than random variances, and an advanced data warehouse that would allow drilling into specific areas of interest. As provider profiling begins to shift away from a primary emphasis on cost to an emphasis on quality, automated methods for measuring guideline compliance will become important in measuring provider performance and increasing guideline usage, consequently improving the standard of care and the potential for better patient outcomes.
This article proposes specific areas of computing competence and illustrates how these skills can be acquired as an integral part of the curriculum of medical genetics. Geneticists are at the forefront in the use of computers for medical care, because of the driving force of the Human Genome Project. Computer searching of international data bases is the most efficient method to keep current with the explosion in molecular genetics data and with its immediate relevance to clinical care. The use of computers in genetics education could go far beyond the use of computer-assisted instruction (CAI) to show how to use computer systems to assist with clinical decisions. The proposed basic computer skills can be obtained using genetics software. The six proposed skills include the use of (1) microcomputers, (2) productivity software, (3) CAI, patient simulations and specific application programs, (4) remote computers, (5) data bases and knowledge bases, and (6) computers to improve the clinical care of patients.
Natural Language Processing in the medical domain becomes more and more powerful, efficient, and ready to be used in daily practice. The needs for such tools are enormous in the medical field, due to the vast amount of written texts for medical records. In the authors' point of view, the Electronic Patient Record (EPR) is achieved neither with Information Systems of all kinds nor with commercially available word processing systems. Natural Language Processing (NLP) is one dimension of the EPR, as well as Image Processing and Decision Support Systems. Analysis of medical texts to facilitate indexing and retrieval is well known. The need for a generation tool is to produce progress notes from menu driven systems. The computer systems of tomorrow cannot miss any single dimension. Since 1988, we've been developing an NLP system; it is supported by the European program AIM (Advanced Informatics in Medicine) within the GALEN and HELIOS consortium and the CERS (Commission d'Encouragement á la Recherche Scientifique) in Switzerland. The main directions of development are: a medical language analyzer, a language generator, a query processor, and dictionary building tools to support the Medical Linguistic Knowledge Base (MLKB). The knowledge representation schema is essentially based on Sowa's conceptual graphs, and the MLKB is multilingual from its design phase; it currently incorporates the English and the French languages; it will also continue using German. The goal of this demonstration is to provide evidence of what exists today, what will be soon available, and what is planned for the long term. Complete sentences will be processed in real time, and the browsing capabilities of the MLKB will be exercised. In particular, the following features will be presented: Analysis of complete sentences with verbs and relatives, as extracted from clinical narratives, with special attention to the method of "proximity processing" as developed in our group and the rule based approach to language description to resolve the specific surface language problems as well as the language independent semantic situations. Comparison of results for English, French, and German sentences, showing the commonalities between these languages and, therefore, the re-usable features and the language specific aspects. Generation of noun phrases in English and French, showing the opportunities for translation between these two languages. Application of the analyzer to build a knowledge representation of ICD under the form of conceptual graphs and presentation of the possibilities of a natural language encoding of diagnosis. Strategies for query processing through a sample of abdominal ultrasonography reports, which have been analyzed and stored under the form of conceptual graphs. Feeding in and browsing of the Medical Linguistic Knowledge Base and other Dictionary Building Tools, using the perspective of an international initiative to converge towards a multilingual universal solution, valid for the medical domain. The demonstration platform is Microsoft Windows 4 on a PC, with Microsoft Visual Basic as the GUI and Quintus Prolog as NLP tools language. The same programs were originally developed for Unix-based workstations and are available on multiple platforms under Motif and X11. .
Pressures to decrease both turnaround time and operating costs simultaneously have placed conflicting demands on traditional forms of medical transcription. The new technology of voice recognition extends the promise of enabling the pathologist or other medical professional to dictate a correct report and have it printed and/or transmitted to a database immediately. The usefulness of voice recognition systems depends on several factors, including ease of use, reliability, speed, and accuracy. These in turn depend on the general underlying design of the systems and inclusion in the systems of a specific knowledge base appropriate for each application. Development of a good knowledge base requires close collaboration between a domain expert and a knowledge engineer with expertise in voice recognition. The authors have recently completed a knowledge base for surgical pathology using the Kurzweil VoiceReport 5,000-word system.
This paper describes a knowledge-based system for molecular diagnostics, and its application to fully automated diagnosis of X-linked genetic disorders. Molecular diagnostic information is used in clinical practice for determining genetic risks, such as carrier determination and prenatal diagnosis. Initially, blood samples are obtained from related individuals, and PCR amplification is performed. Linkage-based molecular diagnosis then entails three data analysis steps. First, for every individual, the alleles (i.e., DNA composition) are determined at specified chromosomal locations. Second, the flow of genetic material among the individuals is established. Third, the probability that a given individual is either a carrier of the disease or affected by the disease is determined. The current practice is to perform each of these three steps manually, which is costly, time consuming, labor-intensive, and error-prone. As such, the knowledge-intensive data analysis and interpretation supersede the actual experimentation effort as the major bottleneck in molecular diagnostics. By examining the human problem solving for the task, we have designed and implemented a prototype knowledge-based system capable of fully automating linkage-based molecular diagnostics in X-linked genetic disorders, including Duchenne Muscular Dystrophy (DMD). Our system uses knowledge-based interpretation of gel electrophoresis images to determine individual DNA marker labels, a constraint satisfaction search for consistent genetic flow among individuals, and a blackboard-style problem solver for risk assessment. We describe the system's successful diagnosis of DMD carrier and affected individuals from raw clinical data.
Based on a historic qualitative study, using documental analyse we searched for the dispersions in the construction of knowledge in the articles of the Anais do Congresso Brasileiro de Enfermagem, from 1977 through 1987. The dispersions emerged as categories that repeat themselves as subjects of note or as smaller punctuation. Our analyze converged into two main categories: professional image and professional identity. These categories took the shape of a complex and dynamic network, in which there were two explicit sub-categories: professional status and professional role.
Computerized decision making aids have been developed for use in certain medical specialties over the last decade. These programs are designed to give expert-level advice within their limited domains. Two major components are required in the design of these systems: development of software and development of a knowledge base to handle the specific medical application. The advantage of the knowledge-based approach is that the application can be changed by replacement of the knowledge base, without requiring changes to the software. In this paper, EMERGE, a medical decision making system, originally developed for analysis of chest pain in the emergency room environment, is described. EMERGE uses production rules for representation of its knowledge base. The method by which this system can accommodate new rule bases for other applications is emphasized, as well as the transportability of the system.
Physicians and other care providers often need to quickly browse and interpret large numbers of time-oriented clinical data. Reducing the information overload involving such tasks is a major goal for medical information systems. We describe a conceptual architecture and software implementation specific to the task of interpretation, summarization, visualization, explanation, and interactive exploration of time-oriented clinical data and the multiple levels of meaningful concepts that can be derived from these data. We build on our work on abstraction of time-oriented clinical data using a knowledge base, acquired from expert physicians, of temporal properties of the data. The core module of the new framework is called KNAVE (Knowledge-based Navigation of Abstractions for Visualization and Explanation). Health care providers can manipulate the display though several visualization and exploration operators. These operators have semantics that are domain independent but that are customized automatically for the application by access to the domain-specific knowledge base. The display, which reflects data and derived interpretations in the patient's database, changes when the user explores key relations (e.g., the dependency hierarchy) in the knowledge base of the relevant clinical domain. Preliminary assessment of the initial prototype with several clinical users has been encouraging. The KNAVE methodology has broad ramifications for reducing the load that large numbers of time-oriented clinical data put on care providers.
Opportunity is the driving force of migration. Unsatisfied demands for higher education and skills, which have been created by the knowledge-based global economy, have generated unprecedented opportunities in knowledge-intensive service industries. These multi-trillion dollar industries include information, communication, finance, business, education and health. The leading industrialized nations are also the focal points of knowledge-intensive service industries and as such constitute centres of research and development activity that proactively draw in talented individuals worldwide through selective immigration policies, employment opportunities and targeted recruitment. Higher education is another major conduit of talent from less-developed countries to the centres of the knowledge-based global economy. Together career and educational opportunities drive "brain drain and recirculation". The departure of a large proportion of the most competent and innovative individuals from developing nations slows the achievement of the critical mass needed to generate the enabling context in which knowledge creation occurs. To favourably modify the asymmetric movement and distribution of global talent, developing countries must implement bold and creative strategies that are backed by national policies to: provide world-class educational opportunities, construct knowledge-based research and development industries, and sustainably finance the required investment for these strategies. Brazil, China and India have moved in this direction, offering world-class education in areas crucial to national development, such as biotechnology and information technology, paralleled by investments in research and development. As a result, only a small proportion of the most highly educated individuals migrate from these countries, and research and development opportunities employ national talent and even attract immigrants.
This study evaluated an intervention based on social cognitive theory (SCT) intended to develop problem-solving skills (PSS) in sixth graders. Psychometrically tested measures were developed for (1) constructs of SCT (situational perception of stressors, expectations of PSS, self-efficacy for PSS, self-efficacy in overcoming barriers, and self control when applying PSS), (2) PSS, and (3) application of PSS to real-life problems. Five classrooms (n = 133) were randomly assigned to the SCT-based intervention and five classrooms (n = 127) to an equivalent knowledge-based intervention. Using a partial nested design, statistically significant improvements for expectations of PSS, self-efficacy for PSS, and PSS were found in the SCT-based intervention. At posttest, 36% of the students in the SCT-based intervention reported applying PSS to real-life problems as compared with 1% in the knowledge-based group. This pilot study suggested that an SCT-based intervention was more efficacious in developing PSS than a knowledge-based intervention.
OBJECTIVES: We present KNAVE-II, an intelligent interface to a distributed architecture specific to the tasks of query, knowledge-based interpretation, summarization, visualization, interactive exploration of large numbers of distributed time-oriented clinical data, and dynamic sensitivity analysis of these data. KNAVE-II main contributions to the fields of temporal reasoning and intelligent user interfaces are: (1) the capability for interactive computation and visualization of domain specific temporal abstractions, supported by ALMA--a computational engine that applies the domain knowledge base to the clinical time-oriented database. (2) Semantic (ontology-based) navigation and exploration of the data, knowledge, and temporal abstractions, supported by the IDAN mediator, a distributed architecture that enables runtime access to domain-specific knowledge bases that are maintained by expert physicians. METHODS AND MATERIALS: KNAVE-II was designed according to 12 requirements that were defined through iterative cycles of design and user-centered evaluation. The complete architecture has been implemented and evaluated in a cross-over study design that compared the KNAVE-II module versus two existing methods: paper charts and an Excel electronic spreadsheet. A small group of clinicians answered the same queries, using the domain of oncology and a set of 1000 patients followed after bone-marrow transplantation. RESULTS: The results show that users are able to perform medium to hard difficulty level queries faster and more accurately by using KNAVE-II than paper charts and Excel. Moreover, KNAVE-II was ranked first in preference by all users, along all usability dimensions. CONCLUSIONS: Initial evaluation of KNAVE-II and its supporting knowledge based temporal-mediation architecture, by applying it to a large data base of patients monitored several years after bone marrow transplantation (BMT), has produced highly encouraging results.
BACKGROUND: Knowledge bases that summarize the published literature provide useful online references for specific areas of systems-level biology that are not otherwise supported by large-scale databases. In the field of neuroanatomy, groups of small focused teams have constructed medium size knowledge bases to summarize the literature describing tract-tracing experiments in several species. Despite years of collation and curation, these databases only provide partial coverage of the available published literature. Given that the scientists reading these papers must all generate the interpretations that would normally be entered into such a system, we attempt here to provide general-purpose annotation tools to make it easy for members of the community to contribute to the task of data collation. RESULTS: In this paper, we describe an open-source, freely available knowledge management system called 'NeuroScholar' that allows straightforward structured markup of the PDF files according to a well-designed schema to capture the essential details of this class of experiment. Although, the example worked through in this paper is quite specific to neuroanatomical connectivity, the design is freely extensible and could conceivably be used to construct local knowledge bases for other experiment types. Knowledge representations of the experiment are also directly linked to the contributing textual fragments from the original research article. Through the use of this system, not only could members of the community contribute to the collation task, but input data can be gathered for automated approaches to permit knowledge acquisition through the use of Natural Language Processing (NLP). CONCLUSION: We present a functional, working tool to permit users to populate knowledge bases for neuroanatomical connectivity data from the literature through the use of structured questionnaires. This system is open-source, fully functional and available for download from [1].
A pulmonary function testing system which automates measurement, storage and reporting as much possible has been functioning in Mitsui Memorial Hospital since 1992. Data obtained from four measurement apparatus are transferred to the central microcomputer by a local area network (LAN). To facilitate automatic interpretation, we developed a knowledge-based system using 87 production rules called 'PADRES', because the knowledge-based system had the following advantages over other systems; (1) The expression of knowledge is understandable and new pieces of knowledge can be easily added, (2) the process of reasoning can be clearly shown, and (3) the accumulation of many instances of data is not required. Using 'PADRES', it takes 2.6 seconds to interpret one set of data on average. Our system performs about 5,000 routine pulmonary function tests per year without faults. Because the program for interpretation is a separate module written in C language, it is readily portable to other operating systems (OS), although its original target was OS-9, a multitask operating system for microcomputers. In conclusion, the application of a knowledge-based system for microcomputers in the pulmonary function test laboratory was considered successful.