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PubMed1997

ADM-INDEX: an automated system for indexing and retrieval of medical texts.

ADM-INDEX is a system for indexing and retrieval of Patients Discharge Summaries (PDSs) by using linguistic methods (morphologic, syntaxic and semantic processing). The ADM-INDEX knowledge base is a restructuring of a diagnostic aid knowledge base (ADM) in order to allow the linguistic analysis of medical texts. The ADM system is a comprehensive medical knowledge base which has been developed since 1972 at the University Hospital of Rennes and which has been the first professional videotex medical diagnostic aid in France. After linguistic analysis, ADM-INDEX build the index table with thesaurus wording, medical words, concepts and phrases, unknown words contained in each PDS. The benefit of using those different elements is to improve information retrieval. Although our system is constructed with the ADM dictionary, it can be easily applied to other medical nomenclature or thesaurus. In this paper, we present on the one hand the ADM-INDEX knowledge base which is constituted by rules, a dictionary and a thesaurus, and on the other hand, the process of indexing and retrieval information.

Abstracting and Indexing↗

PubMed2000

Education for the practice of occupational medicine: knowledge, competence, and professionalism.

The multistep process of education is delineated by the sequential phases: (1) Knowledge Transfer, (2) Competence Development, and (3) Professional Inculcation. The realities of practice modes and curricular time constraints are important determinants of the breadth and depth of the information provided in the Knowledge Transfer process. Accordingly, it is proposed that Phase 1, the Knowledge Acquisition Process, be organized into two components: (1) Core Knowledge, requiring both significant breadth and depth; and (2) Augmentive Knowledge, providing wide breadth and appropriate but variable depth. This curricular organizing proposition recognizes that: (1) the wide breadth of multiple stores of knowledge inherent in the practice of PM and EOM considerably exceeds many other medical specialities; (2) the duration of training is inherently shorter; and (3) its practitioners generally operate as members of teams consisting of other professionals (e.g., attorneys, engineers, business administrators, industrial hygienists, sociologists, psychologists). Obviously, it is unreasonable to expect the members of such teams to each have comparable depth and breadth of knowledge. A broad knowledge base, implicit in Augmentive Knowledge, provides the capacity for recognition, understanding, and application of capabilities brought by other professionals. Facilitating communications between team members, each possessing a broad knowledge base, enhances the effectiveness of the knowledge, competence, and professionalism of collaborative efforts. Phase 2, Competency, consists of the coherent integration of multiple stores of information applicable to the management of a clearly defined task with a clearly measurable outcome. The accomplishment of true competency is not based on the simple possession of multiple stores of knowledge; rather it depends on the facility and effectiveness with which information bases are marshaled, integrated, and communicated. Clearly, the effectiveness of this process increases with its interaction; it is unreasonable to expect a significant degree of competence immediately upon graduation from a training program. Phase 3, Professionalism, and its basic ethos provides the governing context for the sound application of competencies. Although it is difficult to teach, only with its accomplishment can the educational process be considered whole, albeit never complete.

Clinical Competence↗

PubMed2006

Artificial intelligence techniques for monitoring dangerous infections.

The monitoring and detection of nosocomial infections is a very important problem arising in hospitals. A hospital-acquired or nosocomial infection is a disease that develops after admission into the hospital and it is the consequence of a treatment, not necessarily a surgical one, performed by the medical staff. Nosocomial infections are dangerous because they are caused by bacteria which have dangerous (critical) resistance to antibiotics. This problem is very serious all over the world. In Italy, almost 5-8% of the patients admitted into hospitals develop this kind of infection. In order to reduce this figure, policies for controlling infections should be adopted by medical practitioners. In order to support them in this complex task, we have developed a system, called MERCURIO, capable of managing different aspects of the problem. The objectives of this system are the validation of microbiological data and the creation of a real time epidemiological information system. The system is useful for laboratory physicians, because it supports them in the execution of the microbiological analyses; for clinicians, because it supports them in the definition of the prophylaxis, of the most suitable antibi-otic therapy and in monitoring patients' infections; and for epidemiologists, because it allows them to identify outbreaks and to study infection dynamics. In order to achieve these objectives, we have adopted expert system and data mining techniques. We have also integrated a statistical module that monitors the diffusion of nosocomial infections over time in the hospital, and that strictly interacts with the knowledge based module. Data mining techniques have been used for improving the system knowledge base. The knowledge discovery process is not antithetic, but complementary to the one based on manual knowledge elicitation. In order to verify the reliability of the tasks performed by MERCURIO and the usefulness of the knowledge discovery approach, we performed a test based on a dataset of real infection events. In the validation task MERCURIO achieved an accuracy of 98.5%, a sensitivity of 98.5% and a specificity of 99%. In the therapy suggestion task, MERCURIO achieved very high accuracy and specificity as well. The executed test provided many insights to experts, too (we discovered some of their mistakes). The knowledge discovery approach was very effective in validating part of the MERCURIO knowledge base, and also in extending it with new validation rules, confirmed by interviewed microbiologists and specific to the hospital laboratory under consideration.

Artificial Intelligence↗

PubMed1995

Assessing the behavioral impact of a diagnostic decision support system.

This paper describes a prototype for research to evaluate the impact of diagnostic decision support systems on the behavior of physicians. Several indices that can be used to quantify the magnitude of impact are proposed. A large medical diagnostic knowledge base in internal medicine (the Iliad knowledge base) was used in this evaluation. The impact on behavior when different inference models are run against this knowledge base is evaluated for two different case domains and physician's specialties.

Analysis of Variance↗

PubMedDate not supplied

Building a relational database for a physician document index.

We show how three existing medical knowledge bases: Medical Subject Headings (MeSH), Standardized Nomenclature of Medicine (SNOMED) and Current Medical Information and Technology (CMIT) are mapped into a relational data model and stored on an Apollo workstation and an Intelligent Database Machine. Since two of these knowledge bases have been used in the indexing of medical literature and patient records, they can be useful not only as direct views on the organization of medical concepts but also as tools for the retrieval of documents. In order that the concepts from one knowledge base can be connected to those of the other knowledge base, a method has been developed for the semi-automatic merging of MeSH, SNOMED and CMIT. This method takes advantage of the relational model and the synonyms that are given in SNOMED and CMIT, in order to recommend concepts to be merged. An expert interacts with the system to accept or reject the recommendations of the computer. The method would apply equally well to other knowledge bases and is particularly well-suited for knowledge bases that contain tens of thousands of concepts.

Abstracting and Indexing↗

PubMed1993

ASTM E31.15 on health knowledge representation: the Arden Syntax.

ASTM subcommittee E31.15 on Health Knowledge Representation was formed to promote standards for defining and sharing health knowledge bases. Its first standard, the Ardan Syntax, is focused on knowledge bases that can be represented as a set of independent modules called Medical Logic Modules (MLMs). The standard is in clinical use and has generated significant interest in industry and academics. The Extensions task group plans to extend the syntax where appropriate, to expand to other types of knowledge bases. The Validation/Verification task group is approaching the enormous problem of evaluating knowledge bases and the process of sharing them.

Artificial Intelligence↗

PubMed1996

The effects of knowledge availability and knowledge accessibility on coherence and elaborative inferencing in children from six to fifteen years of age.

Two experiments are presented in which a novel knowledge base was acquired by 6- to 15-year-old children prior to hearing a multiepisode story, and where inferences from the story drew only on that knowledge base. Making knowledge equally available to all children did not attenuate age-related differences in either coherence or elaborative inferencing. Easily accessible knowledge was generally twice as likely to be used to make inferences during text comprehension as was knowledge that took longer to retrieve, though knowledge accessibility was more important for coherence inferencing in younger than in older children. Children made more coherence than elaborative inferences in the context of text comprehension, even though elaborative inferencing was more frequent in a simpler processing situation. Within the context of an available knowledge base, the results provide evidence for the importance of knowledge accessibility in children's inferencing, and for the changing developmental relevance of knowledge accessibility for coherence and elaborative inferencing.

Adolescent↗

PubMed1990

Metadata-based generation and management of knowledgebases from molecular biological databases.

Present-day knowledge-based systems (or expert systems) and databases constitute 'islands of computing' with little or no connection to each other. The use of software to provide a communication channel between the two, and to integrate their separate functions, is particularly attractive in certain data-rich domains where there are already pre-existing database systems containing the data required by the relevant knowledge-based system. Our evolving program, GENPRO, provides such a communication channel. The original methodology has been extended to provide interactive Prolog clause input with syntactic and semantic verification. This enables automatic generation of clauses from the source database, together with complete management of subsequent interfacing to the specified knowledge-based system. The particular data-rich domain used in this paper is protein structure, where processes which require reasoning (modelled by knowledge-based systems), such as the inference of protein topology, protein model-building and protein structure prediction, often require large amounts of raw data (i.e., facts about particular proteins) in the form of logic programming ground clauses. These are generated in the proper format by use of the concept of metadata.

Artificial Intelligence↗

PubMed1991

Heuristic determination of quantitative data for knowledge acquisition in medicine.

Knowledge acquisition for medical knowledge bases can be aided by programs that suggest possible values for portions of the data. The paper presents an experiment which was used in designing a heuristic to help the process of knowledge acquisition. The heuristic helps to determine numerical data from stylized literature excerpts in the context of knowledge acquisition for the QMR medical knowledge base. Quantitative suggestions from the heuristics are shown to agree substantially with the data incorporated in the final version of the knowledge base. The experiment shows the potential of knowledge base specific heuristics in simplifying the task of knowledge base creation.

Artificial Intelligence↗

PubMed2000

The construction of autobiographical memories in the self-memory system.

The authors describe a model of autobiographical memory in which memories are transitory mental constructions within a self-memory system (SMS). The SMS contains an autobiographical knowledge base and current goals of the working self. Within the SMS, control processes modulate access to the knowledge base by successively shaping cues used to activate autobiographical memory knowledge structures and, in this way, form specific memories. The relation of the knowledge base to active goals is reciprocal, and the knowledge base "grounds" the goals of the working self. It is shown how this model can be used to draw together a wide range of diverse data from cognitive, social, developmental, personality, clinical, and neuropsychological autobiographical memory research.

Age Factors↗

PubMed2006

A case study in pathway knowledgebase verification.

BACKGROUND: Biological databases and pathway knowledge-bases are proliferating rapidly. We are developing software tools for computer-aided hypothesis design and evaluation, and we would like our tools to take advantage of the information stored in these repositories. But before we can reliably use a pathway knowledge-base as a data source, we need to proofread it to ensure that it can fully support computer-aided information integration and inference. RESULTS: We design a series of logical tests to detect potential problems we might encounter using a particular knowledge-base, the Reactome database, with a particular computer-aided hypothesis evaluation tool, HyBrow. We develop an explicit formal language from the language implicit in the Reactome data format and specify a logic to evaluate models expressed using this language. We use the formalism of finite model theory in this work. We then use this logic to formulate tests for desirable properties (such as completeness, consistency, and well-formedness) for pathways stored in Reactome. We apply these tests to the publicly available Reactome releases (releases 10 through 14) and compare the results, which highlight Reactome's steady improvement in terms of decreasing inconsistencies. We also investigate and discuss Reactome's potential for supporting computer-aided inference tools. CONCLUSION: The case study described in this work demonstrates that it is possible to use our model theory based approach to identify problems one might encounter using a knowledge-base to support hypothesis evaluation tools. The methodology we use is general and is in no way restricted to the specific knowledge-base employed in this case study. Future application of this methodology will enable us to compare pathway resources with respect to the generic properties such resources will need to possess if they are to support automated reasoning.

Algorithms↗

PubMed1996

Computer prediction of possible toxic action from chemical structure: an update on the DEREK system.

Computer-based assessment of potential toxicity has become increasingly popular in recent years. The knowledge-base system DEREK is developed under the guidance of a multinational Collaborative Group of expert toxicologists and provides a qualitative approach to toxicity prediction. Major developments of the DEREK program and knowledge-base have taken place in the last 3 years. Program developments include improvements in both the user interface and data processing. Work on the knowledge-base has concentrated on the areas of genotoxicity and skin sensitisation. DEREK's predictive capabilities for these toxicological end-points has been demonstrated. In addition to the continued expansion of the knowledge-base, a number of enhancements are planned in the DEREK program. In particular, work is in progress to develop further DEREK's ability to report the reasoning behind its predictions.

Animal Testing Alternatives↗

PubMed2000

Automated anatomical labeling of the bronchial branch and its application to the virtual bronchoscopy system.

This paper describes a method for the automated anatomical labeling of the bronchial branch extracted from a three-dimensional (3-D) chest X-ray CT image and its application to a virtual bronchoscopy system (VBS). Automated anatomical labeling is necessary for implementing an advanced computer-aided diagnosis system of 3-D medical images. This method performs the anatomical labeling of the bronchial branch using the knowledge base of the bronchial branch name. The knowledge base holds information on the bronchial branch as a set of rules for its anatomical labeling. A bronchus region is automatically extracted from a given 3-D CT image. A tree structure representing the essential structure of the extracted bronchus is recognized from the bronchus region. Anatomical labeling is performed by comparing this tree structure of the bronchus with the knowledge base. As an application, we implemented the function to automatically present the anatomical names of the branches that are shown in the currently rendered image in real time on the VBS. The result showed that the method could segment about 57% of the branches from CT images and extracted a tree structure of about 91% in branches in the segmented bronchus. The anatomical labeling method could assign the correct branch name to about 93% of the branches in the extracted tree structure. Anatomical names were appropriately displayed in the endoscopic view.

Algorithms↗

PubMed2006

PhenoGO: assigning phenotypic context to gene ontology annotations with natural language processing.

Natural language processing (NLP) is a high throughput technology because it can process vast quantities of text within a reasonable time period. It has the potential to substantially facilitate biomedical research by extracting, linking, and organizing massive amounts of information that occur in biomedical journal articles as well as in textual fields of biological databases. Until recently, much of the work in biological NLP and text mining has revolved around recognizing the occurrence of biomolecular entities in articles, and in extracting particular relationships among the entities. Now, researchers have recognized a need to link the extracted information to ontologies or knowledge bases, which is a more difficult task. One such knowledge base is Gene Ontology annotations (GOA), which significantly increases semantic computations over the function, cellular components and processes of genes. For multicellular organisms, these annotations can be refined with phenotypic context, such as the cell type, tissue, and organ because establishing phenotypic contexts in which a gene is expressed is a crucial step for understanding the development and the molecular underpinning of the pathophysiology of diseases. In this paper, we propose a system, PhenoGO, which automatically augments annotations in GOA with additional context. PhenoGO utilizes an existing NLP system, called BioMedLEE, an existing knowledge-based phenotype organizer system (PhenOS) in conjunction with MeSH indexing and established biomedical ontologies. More specifically, PhenoGO adds phenotypic contextual information to existing associations between gene products and GO terms as specified in GOA. The system also maps the context to identifiers that are associated with different biomedical ontologies, including the UMLS, Cell Ontology, Mouse Anatomy, NCBI taxonomy, GO, and Mammalian Phenotype Ontology. In addition, PhenoGO was evaluated for coding of anatomical and cellular information and assigning the coded phenotypes to the correct GOA; results obtained show that PhenoGO has a precision of 91% and recall of 92%, demonstrating that the PhenoGO NLP system can accurately encode a large number of anatomical and cellular ontologies to GO annotations. The PhenoGO Database may be accessed at the following URL: http://www.phenoGO.org

Computational Biology↗

PubMed1993

Recognition of errors in three-dimensional structures of proteins.

A major problem in the determination of the three-dimensional structure of proteins concerns the quality of the structural models obtained from the interpretation of experimental data. New developments in X-ray crystallography and nuclear magnetic resonance spectroscopy have accelerated the process of structure determination and the biological community is confronted with a steadily increasing number of experimentally determined protein folds. However, in the recent past several experimentally determined protein structures have been proven to contain major errors, indicating that in some cases the interpretation of experimental data is difficult and may yield incorrect models. Such problems can be avoided when computational methods are employed which complement experimental structure determinations. A prerequisite of such computational tools is that they are independent of the parameters obtained from a particular experiment. In addition such techniques are able to support and accelerate experimental structure determinations. Here we present techniques based on knowledge based mean fields which can be used to judge the quality of protein folds. The methods can be used to identify misfolded structures as well as faulty parts of structural models. The techniques are even applicable in cases where only the C alpha trace of a protein conformation is available. The capabilities of the technique are demonstrated using correct and incorrect protein folds.

Models, Molecular↗

PubMed1985

An expert system for nursing practice. Clinical decision support.

An artificial-intelligence-based nursing knowledge base can serve as an expert clinical decision support system in the areas of standards of care, care plans, continuing education, and others for patient conditions with both medical and nursing diagnoses.

Computers↗

PubMed1990

Clinical judgment and therapeutic decision making.

Clinical decision making is under increased scrutiny due to concerns about the cost and quality of medical care. Variability in physician decision making is common, in part because of deficiencies in the knowledge base, but also due to the difference in physicians' approaches to clinical problem solving. Evaluation of patient prognosis is a critical factor in the selection of therapy, and careful attention to methodology is essential to provide reliable information. Randomized controlled clinical trials provide the most solid basis for the establishment of broad therapeutic principles. Because randomized studies cannot be performed to address every question, observational studies will continue to play a complementary role in the evaluation of therapy. Randomized studies in progress, meta analyses of existing data, and increased use of administrative and collaborative clinical data bases will improve the knowledge base for decision making in the future.

Cardiology↗

PubMed2003

Medical errors: legal and ethical responses.

Liability to err is a human, often unavoidable, characteristic. Errors can be classified as skill-based, rule-based, knowledge-based and other errors, such as of judgment. In law, a key distinction is between negligent and non-negligent errors. To describe a mistake as an error of clinical judgment is legally ambiguous, since an error that a physician might have made when acting with ordinary care and the professional skill the physician claims, is not deemed negligent in law. If errors prejudice patients' recovery from treatment and/or future care, in physical or psychological ways, it is legally and ethically required that they be informed of them in appropriate time. Senior colleagues, facility administrators and others such as medical licensing authorities should be informed of serious forms of error, so that preventive education and strategies can be designed. Errors for which clinicians may be legally liable may originate in systemically defective institutional administration.

Humans↗