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Results for “Knowledge base”

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At least 199 records · Page 11Linked to original sources

PubMed1998

Semiautomated segmentation of ovarian follicular ultrasound images using a knowledge-based algorithm.

The application of a knowledge-based segmentation method to the problem of automatically detecting the outer follicle wall boundary in ultrasonographic images of ovaries is presented. A combination of computer detection and interactive adjustment was used to define an approximate inner follicle-wall boundary, which was then used by the computer algorithm as a priori knowledge to automatically find the outer follicle-wall border. The segmentation algorithm was tested on ultrasonographic images of women's ovaries that were imaged in vivo. The semiautomatic segmentations were compared to segmentations by an expert human observer in terms of border placement differences and in terms of quantitative parameters relevant to the physiologic status of the follicles. These physiological parameters include total and specific signal intensity from the follicle and from the follicle wall. The computer-detected outer follicle wall boundaries correlated well with the human observer-defined wall boundaries, in terms of enclosed follicle area, specific and total follicle signal, enclosed wall area, and specific and total wall signal. The actual border placement differences were also small, with a maximum placement difference of 1.47 +/- 0.83 mm and a root mean square (r.m.s.) placement difference of 0.59 +/- 0.28 mm.

Algorithms↗

PubMed1990

The use of knowledge-based information systems for interpreting specialized clinical chemistry analyses--experience from erythrocyte enzymes and metabolites.

A knowledge-based information system has been constructed to facilitate and standardize the interpretation of data obtained from specialized analyses in clinical chemistry. For illustration the system was applied to metabolic studies of erythrocytes from patients in whom hereditary disorders are suspected to explain the presence of a haemolytic anaemia or a polycythaemia. The study includes assay of the catalytic activity of 10 different enzymes and the concentration of some key metabolites. The knowledge-based system is an excellent tool for documentation, updating and transfer of knowledge of the interpretative process. This will reduce the risk of changes in this process being made without sound motivation and documentation. Furthermore, the statistical and graphic features of the system provide data for long-term quality assessment and insights into reference sample groups which are used to update decision levels.

Anemia, Hemolytic↗

PubMedDate not supplied

Knowledge-based interpretation of serologic tests for hepatitis on the World Wide Web.

HEPAXPERT is a knowledge-based system that interprets the results of routine serologic tests for infection with hepatitis A and B viruses. The following tests are included: hepatitis A virus antibodies, IgM antibodies to the hepatitis A virus, hepatitis A virus in stool, hepatitis B surface antigen and antibodies, antibodies to hepatitis B core antigen, and hepatitis B envelope antigen and antibodies. HEPAXPERT/WWW, an implementation of HEPAXPERT-III for the World Wide Web, can be reached by URL http://www.med-expert.co.at/hepax. After selecting HEPAXPERT/WWW, serologic test results can be entered and will be transferred as an E-mail message for subsequent interpretation, which is done off-line with HEPAXPERT-III. The textual interpretation is sent back via E-mail. Each qualitative test for hepatitis A and B antibodies and antigens may produce one of four possible results: positive, negative, borderline, and not tested. To cover the resulting 64 (A) and 57,344 (B) combinations of findings, the knowledge base of HEPAXPERT/WWW contains 16 rules of hepatitis A and 131 rules for hepatitis B serology interpretation. This basic knowledge is structured such that all possible combinations of findings can be interpreted, and there is no overlap in the premises underlying the rules. The reports that the system automatically generates include the transferred results of the tests; a detailed analysis of the results, including virus exposure, immunity, stage of illness, prognosis, infectiousness, and vaccination recommendation; and, as an option, an identification, to distinguish the origin of the interpretation requests.

Austria↗

PubMed1990

Quantitative cytomorphology of megakaryocytes in chronic myeloproliferative disorders--analysis of planimetric and numeric characteristics by means of a knowledge based system.

Numeric and planimetric parameters of megakaryocytes have been analyzed in 162 bone marrow biopsies of patients with chronic myeloproliferative disorders--CMPD--and controls by means of an inductive knowledge-based system in combination with a multivariate data analysis. To achieve a reliable differential diagnosis between the different entities of CMPD and controls, decision trees and the rank order of the best discriminating parameters have been calculated. The cases measured were defined by 3 histopathologists who were involved in the elaboration of the Hannover Classification of CMPD. The results demonstrate striking numeric and morphologic characteristics of the megakaryopoiesis in each separate primary category of CMPD, that is (1) chronic myeloid leukemia of the common type and (2) with megakaryocytic increase, (3) polycythemia vera, (4) primary or idiopathic thrombocythemia, and (5) chronic megakaryocytic-granulocytic myelosis. Thus, the morphometric measurements did confirm the validity of the Hannover Classification of CMPDs. In order to evaluate the information contained in large quantitative and semiquantitative data bases and diagnostic decisions, knowledge-based expert systems seem to represent a valuable addition to conventional statistics.

Cell Count↗

PubMed1997

RIBOWEB: linking structural computations to a knowledge base of published experimental data.

The world wide web (WWW) has become critical for storing and disseminating biological data. It offers an additional opportunity, however, to support distributed computation and sharing of results. Currently, computational analysis tools are often separated from the data in a manner that makes iterative hypothesis testing cumbersome. We hypothesize that the cycle of scientific reasoning (using data to build models, and evaluating models in light of data) can be facilitated with resources that link computations with semantic models of the data. Riboweb is an on-line knowledge-based resource that supports the creation of three-dimensional models of the 30S ribosomal subunit. It has three components: (I) a knowledge base containing representations of the essential physical components and published structural data, (II) computational modules that use the knowledge base to build or analyze structural models, and (III) a web-based user interface that supports multiple users, sessions and computations. We have built a prototype of Riboweb, and have used it to refine a rough model of the central domain of the 30S subunit from E. coli. procedure. Our results suggest that sophisticated and integrated computational capabilities can be delivered to biologists using this simple three-component architecture.

Artificial Intelligence↗

PubMed1990

A knowledge-based system for transfusion advice.

A knowledge-based system has been designed for evaluating the appropriateness of transfusion of non-red blood cell blood components. The goal of the system is to assist the blood bank physician in quality assurance efforts by automatically identifying cases of inappropriate transfusion before the blood is issued. Evaluation of a working prototype system shows that it is indeed capable of serving this function. The system identifies and summarizes cases, but it leaves consultation, education, and decision making to the blood bank physician. Small "expert systems" such as this may find use in quality assurance activities throughout the laboratory.

Blood Transfusion↗

PubMed2004

Development of an expert system knowledge base: a novel approach to promote guideline congruent asthma care.

Existing guidelines for the clinical management of asthma provide a good framework for such tasks as diagnosing asthma, determining severity, and prescribing pharmacological treatment. Guidance is less explicit, however, about establishing a patient-provider partnership and overcoming barriers to asthma management by patients in a way that can be easily adopted in clinical practice. We report herein the first developmental phase of the "Stop Asthma" expert system. We describe the establishment of a knowledge base related to both the clinical management of asthma and the enhancement of patient and family self-management (including environmental management). The resultant knowledge base comprises 142 multilayered decision rules that describe clinical and behavioral management in three domains: 1) determination of asthma severity and control; 2) pharmacotherapy, including prescription of medicine for chronic maintenance, acute exacerbation, exercise pretreatment, and rhinitis relief; and 3) patient self-management, including the process of intervening to facilitate the patient's asthma medication management, environmental control, and well-visit scheduling. The knowledge base provides a systematic and accessible approach for intervening with family asthma-related behaviors.

Asthma↗

PubMed2001

A knowledge-based system for patient image pre-fetching in heterogeneous database environments--modeling, design, and evaluation.

When performing primary reading on a newly taken radiological examination, a radiologist often needs to reference relevant prior images of the same patient for confirmation or comparison purposes. Support of such image references is of clinical importance and may have significant effects on radiologists' examination reading efficiency, service quality, and work satisfaction. To effectively support such image reference needs, we proposed and developed a knowledge-based patient image pre-fetching system, addressing several challenging requirements of the application that include representation and learning of image reference heuristics and management of data-intensive knowledge inferencing. Moreover, the system demands an extensible and maintainable architecture design capable of effectively adapting to a dynamic environment characterized by heterogeneous and autonomous data source systems. In this paper, we developed a synthesized object-oriented entity- relationship model, a conceptual model appropriate for representing radiologists' prior image reference heuristics that are heuristic oriented and data intensive. We detailed the system architecture and design of the knowledge-based patient image pre-fetching system. Our architecture design is based on a client-mediator-server framework, capable of coping with a dynamic environment characterized by distributed, heterogeneous, and highly autonomous data source systems. To adapt to changes in radiologists' patient prior image reference heuristics, ID3-based multidecision-tree induction and CN2-based multidecision induction learning techniques were developed and evaluated. Experimentally, we examined effects of the pre-fetching system we created on radiologists' examination readings. Preliminary results show that the knowledge-based patient image pre-fetching system more accurately supports radiologists' patient prior image reference needs than the current practice adopted at the study site and that radiologists may become more efficient, consultatively effective, and better satisfied when supported by the pre-fetching system than when relying on the study site's pre-fetching practice.

Artificial Intelligence↗

PubMed1989

The data dictionary--a controlled vocabulary for integrating clinical databases and medical knowledge bases.

The medical information systems of the future will probably include the entire medical record as well as a knowledge base, providing decision support for the physician during patient care. Data dictionaries will play an important role in integrating the medical knowledge bases with the clinical databases. This article presents an infological data model of such an integrated medical information system. Medical events, medical terms, and medical facts are the basic concepts that constitute the model. To allow the transfer of information and knowledge between systems, the data dictionary should be organized with regard to several common classification schemes of medical nomenclature.

Database Management Systems↗

PubMed1999

Knowledge-based expert systems for toxicity and metabolism prediction: DEREK, StAR and METEOR.

It has long been recognised that the ability to predict the metabolic fate of a chemical substance and the potential toxicity of either the parent compound or its metabolites are important in novel drug design. The popularity of using computer models as an aid in this area has grown considerably in recent years. LHASA Limited has been developing knowledge-based expert systems for toxicity and metabolism prediction in collaboration with industry and regulatory authorities. These systems, DEREK, StAR and METEOR, use rules to describe the relationship between chemical structure and either toxicity in the case of DEREK and StAR, or metabolic fate in the case of METEOR. The rule refinement process for DEREK often involves assessing the predictions for a novel set of compounds and comparing them to their biological assay results as a measure of the system's performance. For example, 266 non-congeneric chemicals from the National Toxicology Program database have been processed through the DEREK mutagenicity knowledge base and the predictions compared to their Salmonella typhimurium mutagenicity data. Initially, 81 of 114 mutagens (71%) and 117 of 152 non-mutagens (77%) were correctly identified. Following further knowledge base development, the number of correctly identified mutagens has increased to 96 (84%). Further work on improving the predictive capabilities of DEREK, StAR and METEOR is in progress.

Computer Simulation↗

PubMed1990

Extracting knowledge from a large primary health care database using a knowledge-based statistical approach.

Clinical databases from automated medical records represent a growing resource for deriving new medical knowledge. In this study a large primary health care database was explored with respect to the association between hypertension and diabetes. Data collection was made with a query language, and data analysis performed with an interactive knowledge-based statistical tool, MAXITAB, employing a multivariate tabular analysis technique. In the study population of 6660 patients the prevalence of diabetes was almost three times higher for hypertensive patients than for those with no hypertension. Conversely, the prevalence of hypertension was 2.6 times higher for diabetic patients than for those with no diabetes. The results support the assumption of a relationship between hypertension and diabetes, although the question of causality between the two diagnoses remains unsolved. Knowledge-based statistical tools of this kind may be feasible for exploring large clinical databases and may result in new medical hypotheses, worthy of further investigation.

Aged↗

PubMed2007

[Public Health Genomics. The integration of genome-based knowledge into public health research, policies and health services].

Which consequences can be drawn from genome-based knowledge and how can it be responsibly and timely translated into policies and practice? What are recent developments in genetics and molecular biology, what are the challenges, what are the risks of these developments? Which policies can provide an acceptable balance between providing strong protection of individuals'interests and needs while enabling society to benefit from the genomic advances and empowering individuals? How can molecular medicine contribute to more effective and efficient health care services, and what infrastructures and policies can already now be implemented to assure a benefit for population health? Thus, Public Health Genomics (PHG) tries to answer these challenging questions. This integration of genomics into the aims of public health is called Public Health Genomics (PHG) and is defined as "the responsible and effective translation of genome-based knowledge and technologies into public policy and health services for the benefit of population health".

Delivery of Health Care, Integrated↗

PubMed2003

Ligand-supported homology modelling of protein binding-sites using knowledge-based potentials.

A new approach, MOBILE, is presented that models protein binding-sites including bound ligand molecules as restraints. Initially generated, homology models of the target protein are refined iteratively by including information about bioactive ligands as spatial restraints and optimising the mutual interactions between the ligands and the binding-sites. Thus optimised models can be used for structure-based drug design and virtual screening. In a first step, ligands are docked into an averaged ensemble of crude homology models of the target protein. In the next step, improved homology models are generated, considering explicitly the previously placed ligands by defining restraints between protein and ligand atoms. These restraints are expressed in terms of knowledge-based distance-dependent pair potentials, which were compiled from crystallographically determined protein-ligand complexes. Subsequently, the most favourable models are selected by ranking the interactions between the ligands and the generated pockets using these potentials. Final models are obtained by selecting the best-ranked side-chain conformers from various models, followed by an energy optimisation of the entire complex using a common force-field. Application of the knowledge-based pair potentials proved efficient to restrain the homology modelling process and to score and optimise the modelled protein-ligand complexes. For a test set of 46 protein-ligand complexes, taken from the Protein Data Bank (PDB), the success rate of producing near-native binding-site geometries (rmsd<2.0A) with MODELLER is 70% when the ligand restrains the homology modelling process in its native orientation. Scoring these complexes with the knowledge-based potentials, in 66% of the cases a pose with rmsd <2.0A is found on rank 1. Finally, MOBILE has been applied to two case studies modelling factor Xa based on trypsin and aldose reductase based on aldehyde reductase.

Binding Sites↗

PubMed1988

An expert system for the early detection of melanoma using knowledge-based image analysis.

Melanoma is the most lethal skin cancer; however, nearly all patients can be saved and cured by early detection and prompt surgical treatment. It has been demonstrated that the major diagnostic and prognostic parameters of melanoma are the vertical thickness, three-dimensional (3D) size and shape, and color of the lesion. The other characteristic features of early melanoma are irregularities in the boundary of the lesion and the appearance of nonuniform pigmentation (with a variety of color). During early stages of development of the melanoma, the changes in these parameters are very difficult to assess since no good tool exists for measuring them in situ and analyzing them for malignancy. A novel optical instrument called the "Nevoscope" has been developed to obtain multiple views of the transilluminated skin lesion from several angles. These views have been used to measure the thickness and 3D size of the skin lesion without excision. A knowledge-based image analysis and interpretation system is being developed to analyze images of the skin lesion for a set of diagnostic and prognostic features: thickness, 3D size, color and margin, boundary and surface characteristics. This analysis combined with the patient's history, such as occurrence of melanoma or dysplastic nevi in the family, life style, skin type, etc., is used by the knowledge-based expert system to detect early or potentially malignant lesions. The diagnostic and prognostic knowledge bases for the early detection of melanoma are being developed with the help of expert dermatologists and published case studies.

Algorithms↗

PubMed2004

Single-body residue-level knowledge-based energy score combined with sequence-profile and secondary structure information for fold recognition.

An elaborate knowledge-based energy function is designed for fold recognition. It is a residue-level single-body potential so that highly efficient dynamic programming method can be used for alignment optimization. It contains a backbone torsion term, a buried surface term, and a contact-energy term. The energy score combined with sequence profile and secondary structure information leads to an algorithm called SPARKS (Sequence, secondary structure Profiles and Residue-level Knowledge-based energy Score) for fold recognition. Compared with the popular PSI-BLAST, SPARKS is 21% more accurate in sequence-sequence alignment in ProSup benchmark and 10%, 25%, and 20% more sensitive in detecting the family, superfamily, fold similarities in the Lindahl benchmark, respectively. Moreover, it is one of the best methods for sensitivity (the number of correctly recognized proteins), alignment accuracy (based on the MaxSub score), and specificity (the average number of correctly recognized proteins whose scores are higher than the first false positives) in LiveBench 7 among more than twenty servers of non-consensus methods. The simple algorithm used in SPARKS has the potential for further improvement. This highly efficient method can be used for fold recognition on genomic scales. A web server is established for academic users on http://theory.med.buffalo.edu.

Algorithms↗

PubMedDate not supplied

Knowledge base and preferred methods of obtaining knowledge of glaucoma patients.

PURPOSE: To gather information regarding patient's understanding of glaucoma and the manner in which patients wish to learn about the disease with the intent of improving patient education. METHODS: Forty-four of sixty randomly selected ophthalmologists (73%) asked four of their patients consecutively to complete a questionnaire about glaucoma. The selection of questions was based on focus group interviews and suggestions from several experts. Topics included knowledge about glaucoma and its treatment, the need for information, and preferred providers and methods of patient education. RESULTS: Fifty percent of the patients had 49% or less correct answers to questions about glaucoma or its treatment. Per item the correct answers ranged from 5% to 90%. Lack of knowledge was associated with low level of education, short duration of glaucoma, high age, and no preference for the Internet as method of supplying information. These variables, however, did not identify groups with a considerable lack of knowledge sufficiently accurately to target patient education. A high need for information was observed and included information about the patient's own glaucoma. Almost all patients preferred the ophthalmologist and many also a nurse or a representative of the Glaucoma Patient Society as providers of information. Written material was the preferred method. CONCLUSIONS: Patient education should address all patients. A patient education program should cover a wide range of topics with a focus on general information through written material and information tailored to the individual glaucoma patient's needs. The ophthalmologist is a key- person, but others could play an important role in patient education.

Adult↗

PubMed2004

A knowledge-based framework for image enhancement in aviation security.

The main aim of this paper is to present a knowledge-based framework for automatically selecting the best image enhancement algorithm from several available on a per image basis in the context of X-ray images of airport luggage. The approach detailed involves a system that learns to map image features that represent its viewability to one or more chosen enhancement algorithms. Viewability measures have been developed to provide an automatic check on the quality of the enhanced image, i.e., is it really enhanced? The choice is based on ground-truth information generated by human X-ray screening experts. Such a system, for a new image, predicts the best-suited enhancement algorithm. Our research details the various characteristics of the knowledge-based system and shows extensive results on real images.

Algorithms↗

PubMed1996

Knowledge retrieval as one type of knowledge-based decision support in medicine: results of an evaluation study.

We report on a prospective, prolective observational study, supplying information on how physicians and other health care professionals retrieve medical knowledge on-line within the Heidelberg University Hospital information system. Within this hospital information system, on-line access to medical knowledge has been realised by installing a medical knowledge server in the range of about 24 GB and by providing access to it by health care professional workstations in wards, physicians' rooms, etc. During the study, we observed about 96 accesses per working day. The main group of health care professionals retrieving medical knowledge were physicians and medical students. Primary reasons for its utilisation were identified as support for the users' scientific work (50%), own clinical cases (19%), general medical problems (14%) and current clinical problems (13%). Health care professionals had accesses to medical knowledge bases such as MEDLINE (79%), drug bases ('Rote Liste', 6%), and to electronic text books and knowledge base systems as well. Sixty-five percent of accesses to medical knowledge were judged to be successful. In our opinion, medical knowledge retrieval can serve as a first step towards knowledge processing in medicine. We point out the consequences for the management of hospital information systems in order to provide the prerequisites for such a type of knowledge retrieval.

Computer Systems↗