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Results for “Knowledge base”

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At least 253 records · Page 14Linked to original sources

PubMed1990

Knowledge-based prediction of protein structures.

We propose a knowledge-based approach to the prediction of protein structures in cases where there is no sequence-homology to proteins with known spatial structure. Using methods from Artificial Intelligence we attempt to take into account long-range interactions within the prediction process. This allows not only the assignment of secondary but also of supersecondary structure elements. In particular, the patterns used as conditions of prediction rules are generated by learning methods from information contained in the Protein Data Base. Patterns on higher levels of the protein structure hierarchy are used as constraints to reduce the combinatorial search space. These patterns may also be used to search for specified structure motifs by interactive retrieval.

Artificial Intelligence↗

PubMed1997

A comprehensive knowledge-based system for laboratory hematology.

The Coulter FACULTY knowledge-based systems, Professor Petrushka for peripheral blood interpretation, Professor Fidelio for flow cytometry immunophenotyping and Professor Belmonte for bone marrow reporting, have been installed in several hospitals in Spain, Portugal and the United Kingdom. In Spain and Portugal, the systems are part of the IZASA-Coulter CITOTECA workstation, which includes a video camera for capturing microscopic images and a networkable laboratory information system supporting color reports. At the Royal Hospitals Trust (St. Bartholomew's Hospital and The Royal London Hospital, London, UK), networked workstations are available and the system is used daily to generate bone marrow reports in the hematology laboratories. There have been considerable benefits from adopting Coulter FACULTY for bone marrow reporting, including faster turnaround time, improved quality of the reports and cost savings.

Artificial Intelligence↗

PubMed1991

Probabilistic diagnosis using a reformulation of the INTERNIST-1/QMR knowledge base. I. The probabilistic model and inference algorithms.

In Part I of this two-part series, we report the design of a probabilistic reformulation of the Quick Medical Reference (QMR) diagnostic decision-support tool. We describe a two-level multiply connected belief-network representation of the QMR knowledge base of internal medicine. In the belief-network representation of the QMR knowledge base, we use probabilities derived from the QMR disease profiles, from QMR imports of findings, and from National Center for Health Statistics hospital-discharge statistics. We use a stochastic simulation algorithm for inference on the belief network. This algorithm computes estimates of the posterior marginal probabilities of diseases given a set of findings. In Part II of the series, we compare the performance of QMR to that of our probabilistic system on cases abstracted from continuing medical education materials from Scientific American Medicine. In addition, we analyze empirically several components of the probabilistic model and simulation algorithm.

Algorithms↗

PubMedDate not supplied

Differences that make a difference: comparisons of metacomponential functioning and knowledge base among groups of high and low IQ learning disabled, mildly mentally retarded, and normally achieving adults.

To determine if cognition among persons with learning disabilities (LD) and mild mental retardation (MMR) is similar, we compared metacomponential functioning and knowledge acquisition across groups of incarcerated adults with LD and high IQ (HIQLD), with LD and low IQ (LIQLD), with normal achievement (NA), and with MMR. The Slosson Intelligence Test Computer Report (Nicholson, 1984) formula established criteria for group inclusion. Metacomponential functioning among 77 males and 26 females was measured by a confidence test (Echternacht, Boldt, & Sellman, 1971) designed for the general knowledge subtest of the SRA Achievement Battery (Naslund, Thorpe, & Lefever, 1982). Knowledge base and group membership were significantly related to metacomponential ability (R2 = .84). Persons with HIQLD and LIQLD performed better than those with MMR on both measures. The HIQLD, however, did not outperform their peers with NA. Results show that (a) knowledge base is the best predictor of metacomponential skill, (b) metacomponential orchestration differentiates persons with HIQLD from those with LIQLD and both groups from persons with MMR, and (c) IQ mediates metacognition, but does not explain it. Education should emphasize knowledge acquisition for people with HIQLD; people with LIQLD and MMR require more attention to metacognition.

Achievement↗

PubMed2001

Knowledge-based and data-driven models in arrhythmia fuzzy classification.

OBJECTIVES: Fuzzy rules automatically derived from a set of training examples quite often produce better classification results than fuzzy rules translated from medical knowledge. This study aims to investigate the difference in domain representation between a knowledge-based and a data-driven fuzzy system applied to an electrocardiography classification problem. METHODS: For a three-class electrocardiographic arrhythmia classification task a set of fifteen fuzzy rules is derived from medical expertise on the basis of twelve electrocardiographic measures. A second set of fuzzy rules is automatically constructed on thirty-nine MIT-BIH database's records. The performances of the two classifiers on thirteen different records are comparable and up to a certain extent complementary. The two fuzzy models are then analyzed, by using the concept of information gain to estimate the impact of each ECG measure on each fuzzy decision process. RESULTS: Both systems rely on the beat prematurity degree and the QRS complex width and neglect the P wave existence and the ST segment features. The PR interval is not well characterized across the fuzzy medical rules while it plays an important role in the data-driven fuzzy system. The T wave area shows a higher information gain in the knowledge based decision process, and is not very much exploited by the data-driven system. CONCLUSIONS: The main difference between a human designed and a data driven ECG arrhythmia classifier is found about the PR interval and the T wave.

Arrhythmias, Cardiac↗

PubMedDate not supplied

Knowledge-based patient screening for rare and emerging infectious/parasitic diseases: a case study of brucellosis and murine typhus.

Many infectious and parasitic diseases, especially those newly emerging or reemerging, present a difficult diagnostic challenge because of their obscurity and low incidence. Important clues that could lead to an initial diagnosis are often overlooked, misinterpreted, not linked to a disease, or disregarded. We constructed a computer-based decision support system containing 223 infectious and parasitic diseases and used it to conduct a historical intervention study based on field investigation records of 200 cases of human brucellosis and 96 cases of murine typhus that occurred in Texas from 1980 through 1989. Knowledge-based screening showed that the average number of days from the initial patient visit to the time of correct diagnosis was significantly reduced (brucellosis-from 17.9 to 4.5 days, p = 0.0001, murine typhus-from 11.5 to 8.6 days, p = 0.001). This study demonstrates the potential value of knowledge-based patient screening for rare infectious and parasitic diseases.

Artificial Intelligence↗

PubMedDate not supplied

The promise of a new technology: knowledge-based systems in radiation oncology and diagnostic radiology.

The revolutionary changes in computer capabilities in the last decade, both in software and hardware, have opened new doorways for the uses of computers in radiation oncology and diagnostic radiology. Knowledge-based systems offer the potential to function as aids, consultants and advisors in the differential diagnosis of disease, staging, selection of therapy and treatment management and delivery for cancer patients. These computer-based systems can also provide for the training and teaching of radiotherapy and diagnostic radiology residents, and act as advisors and teachers to the medical physicists, dosimetrists and technicians. Following a brief history of the development of knowledge-based systems, the general capabilities of computer-based physician workstations in a department of radiation oncology are described.

Expert Systems↗

PubMed2000

Knowledge-based grouping of modeled HLA peptide complexes.

Human leukocyte antigens are the most polymorphic of human genes and multiple sequence alignment shows that such polymorphisms are clustered in the functional peptide binding domains. Because of such polymorphism among the peptide binding residues, the prediction of peptides that bind to specific HLA molecules is very difficult. In recent years two different types of computer based prediction methods have been developed and both the methods have their own advantages and disadvantages. The nonavailability of allele specific binding data restricts the use of knowledge-based prediction methods for a wide range of HLA alleles. Alternatively, the modeling scheme appears to be a promising predictive tool for the selection of peptides that bind to specific HLA molecules. The scoring of the modeled HLA-peptide complexes is a major concern. The use of knowledge based rules (van der Waals clashes and solvent exposed hydrophobic residues) to distinguish binders from nonbinders is applied in the present study. The rules based on (1) number of observed atomic clashes between the modeled peptide and the HLA structure, and (2) number of solvent exposed hydrophobic residues on the modeled peptide effectively discriminate experimentally known binders from poor/nonbinders. Solved crystal complexes show no vdW Clash (vdWC) in 95% cases and no solvent exposed hydrophobic peptide residues (SEHPR) were seen in 86% cases. In our attempt to compare experimental binding data with the predicted scores by this scoring scheme, 77% of the peptides are correctly grouped as good binders with a sensitivity of 71%.

Alleles↗

PubMed1989

A knowledge-based system for automatic interpretation of an analytical profile of complement factors.

A comprehensive assay to evaluate the complement system includes functional tests of both classical and alternative pathways and immunochemical measurements of C3, C4, B, C1-INA, and C3d. The purpose of this analytical profile is to screen for rare hereditary deficiencies and acquired abnormalities of complement and to define the activation pathway in cases of complement consumptive processes. Based on several years' experience, a routine was established in our laboratory to report the data to the clinician, together with a computer-generated interpretive statement. This routine was formulated into a knowledge base by specifying a series of decision rules for each of the complement disorders. After the rules were tested and updated against some 400 complement profile analyses, reasonable analytical comments were produced by the system. This knowledge-based system for reporting and interpreting complement results offers several advantages: the interpretative work is facilitated and made more reliable; a consistent interpretative comment is generated that is recognized and therefore more meaningful for the clinician; the communication of analytical procedures and policy is enhanced.

Artificial Intelligence↗

PubMed1994

Creating a knowledge base of biological research papers.

Intelligent text-oriented tools for representing and searching the biological research literature are being developed, which combine object-oriented databases with artificial intelligence techniques to create a richly structured knowledge based of Materials and Methods sections of biological research papers. A knowledge model of experimental processes, biological and chemical substances, and analytical techniques is described, based on the representation techniques of taxonomic semantic nets and knowledge frames. Two approaches to populating the knowledge base with the contents of biological research papers are described: natural language processing and an interactive knowledge definition tool.

Algorithms↗

PubMed1994

Knowledge-based system for the automated solid-phase extraction of basic drugs from plasma coupled with their liquid chromatographic determination. Application to the biodetermination of beta-receptor blocking agents.

Techniques for the preparation of biological samples are often based nowadays on solid-phase extraction (SPE). The different SPE steps can be performed automatically on disposable extraction cartridges (DECs) by means of a sample processor. A knowledge-based system was developed to facilitate the development of fully automated methods for the solid-phase extraction of relatively hydrophobic basic drugs from plasma, coupled with their determination by high-performance liquid chromatography (HPLC). The DEC filled with 50 mg of cyanopropyl-bonded silica phase is first conditioned with methanol and buffer solution (pH 7.4). After sample application, the DEC sorbent is washed with the same buffer. The analytes are then desorbed with an appropriate eluent and the eluate is finally diluted with the same buffer as used in the HPLC mobile phase before injection. Under these conditions, only three variables are still to be optimized: the composition and volume of the elution solvent and the volume of buffer to be added to the eluate. On the basis of this general strategy, a decision tree providing information about suggested starting conditions and guidelines for the optimization of the three variables was developed and implemented by use of a hypermedia software. This didactic expert system was evaluated using several beta-receptor blocking agents as model compounds and the operating conditions obtained for the automated SPE of these compounds are presented. A method for the determination of propranolol in plasma using the SPE conditions deduced from the knowledge-based system was validated. The absolute recovery of propranolol is ca. 93% and the limit of detection is 1.3 ng ml-1. The mean within-day and between-day reproducibilities are 2.3 and 3.6%, respectively.

Adrenergic beta-Antagonists↗

PubMed1992

A framework for the knowledge-based interpretation of laboratory data in intensive care units using deductive database technology.

In co-operation with the Institute of Anaesthesiology of the Ludwig-Maximilians-University in Munich a computer-based system for the analysis and interpretation of renal function, fluid and electrolyte metabolism of critical care patients has been developed. This paper focuses on the requirements and implementation aspects of the knowledge-based interpretation for this particular system. Objective of the proposed approach is, to transform an enormous--and constantly increasing--amount of raw data available in modern intensive care units (ICUs) into relevant, patient-oriented information, which is easy to understand by the medical staff. The essential features of a knowledge-based system at an ICU are outlined. A system is described where these features are realized using deductive database technology as a specification paradigm and extended relational databases as an implementation platform. The integration into the hospital information system is highlighted.

Artificial Intelligence↗

PubMed2002

A knowledge-based approach to automatic detection of the spinal cord in CT images.

Accurate planning of radiation therapy entails the definition of treatment volumes and a clear delimitation of normal tissue of which unnecessary exposure should be prevented. The spinal cord is a radiosensitive organ, which should be precisely identified because an overexposure to radiation may lead to undesired complications for the patient such as neuronal disfunction or paralysis. In this paper, a knowledge-based approach to identifying the spinal cord in computed tomography images of the thorax is presented. The approach relies on a knowledge-base which consists of a so-called anatomical structures map (ASM) and a task-oriented architecture called the plan solver. The ASM contains a frame-like knowledge representation of the macro-anatomy in the human thorax. The plan solver is responsible for determining the position, orientation and size of the structures of interest to radiation therapy. The plan solver relies on a number of image processing operators. Some are so-called atomic (e.g., thresholding and snakes) whereas others are composite. The whole system has been implemented on a standard PC. Experiments performed on the image material from 23 patients show that the approach results in a reliable recognition of the spinal cord (92% accuracy) and the spinal canal (85% accuracy). The lamina is more problematic to locate correctly (accuracy 72%). The position of the outer thorax is always determined correctly.

Adult↗

PubMed2005

Prediction of protein thermostability with a direction- and distance-dependent knowledge-based potential.

The increasing use of enzymes in industrial processes and the importance of understanding protein folding and stability have led to several attempts to predict and quantify the effect of every possible amino acid exchange (mutation) on the thermostability of proteins. In this article we describe a knowledge-based discrimination function that acts as a fast and reliable guide in protein engineering and optimization. The function used consists of two parts, a pairwise energy function based on a distance- and direction-dependent atomic description of the amino acid environment, and a torsion angle energy function. In a first step a training set of 11 proteins including 646 mutant proteins with experimentally determined thermostability was used to optimize the knowledge-based energy functions. The resulting potential function was then tested using a test mutant database consisting of 918 various point mutations introduced in 27 proteins. The best correlation coefficient obtained for the experimental data and the predicted thermostability for the training set is r = 0.81 (561 data points). A total of 76% of the mutations could be predicted correctly as being either stabilizing or destabilizing. The results for the test set are r = 0.74 (747 data points) and 72%, respectively. The global correlation over the combined data (1308 mutants) obtained is 0.78.

Databases, Protein↗

PubMed2001

Reporting cerebrospinal fluid data: knowledge base and interpretation software.

The compilation of cerebrospinal fluid (CSF) patient data together with a graphic display of immunoglobulin patterns in a single CSF report has two main advantages: analytical and clinical plausibility control of a complex set of data improves quality assessment and allows improved clinical specificity and sensitivity for recognition of disease-related "typical" data patterns. The widespread use of automated on-line evaluation programs can now be combined with knowledge-based programs for interpretation by clinical chemists and neurologists. These programs are based on knowledge of neuroimmunology, blood-CSF barrier function and dysfunction, influence of CSF flow on concentrations of blood-derived and brain-derived proteins in CSF, specific intrathecal antibody synthesis and relevance of brain proteins for differential diagnosis of degenerative diseases. The relevance of hyperbolic discrimination functions in quotient diagrams for the detection of intrathecal immunoglobulin synthesis is compared with earlier, still frequently used, linear interpretation functions. Differences found in commercially available interpretation software are discussed.

Brain↗

PubMed2002

When action turns into words. Activation of motor-based knowledge during categorization of manipulable objects.

Functional imaging studies have demonstrated that processing of man-made objects activate the left ventral premotor cortex, which is known to be concerned with motor function. This has led to the suggestion that the comprehension of man-made objects may rely on motor-based knowledge of object utilization (action knowledge). Here we show that the left ventral premotor cortex is activated during categorization of "both" fruit/vegetables and articles of clothing, relative to animals and nonmanipulable man-made objects. This observation suggests that action knowledge may not be important for the processing of man-made objects per se, but rather for the processing of manipulable objects in general, whether natural or man-made. These findings both support psycholinguistic theories suggesting that certain lexical categories may evolve from, and the act of categorization rely upon, motor-based knowledge of action equivalency, and have important implications for theories of category specificity. Thus, the finding that the processing of vegetables/fruit and articles of clothing give rise to similar activation is difficult to account for should knowledge representations in the brain be truly categorically organized. Instead, the data are compatible with the suggestion that categories differ in the weight they put on different types of knowledge.

Concept Formation↗

PubMed2002

ASExpert: an integrated knowledge-based system for activated sludge plants.

The activated sludge process is commonly used for secondary wastewater treatment worldwide. This process is capable of achieving high quality effluent. However it has the reputation of being difficult to operate because of its poorly understood biological behaviour, variability of input flows and the need to incorporate qualitative data. To augment this incomplete knowledge with experience, knowledge-based systems were introduced in the 1980s however they didn't receive much popularity. This paper presents the Activated Sludge Expert system (ASExpert), which is a rule-based expert system plus a complete database tool proposed for use in activated sludge plants. The paper focuses on presenting the system's main features and capabilities to revive the interest in knowledge-based systems as a reliable means for monitoring plants. Then it presents the methodology adopted for ASExpert validation along with an assessment of testing results. Finally it concludes that expert systems technology has proved its importance for enhancing performance, especially if in the future it is integrated to a modern control system.

Bioreactors↗

PubMed2001

Calculating the knowledge-based similarity of functional groups using crystallographic data.

A knowledge-based method for calculating the similarity of functional groups is described and validated. The method is based on experimental information derived from small molecule crystal structures. These data are used in the form of scatterplots that show the likelihood of a non-bonded interaction being formed between functional group A (the 'central group') and functional group B (the 'contact group' or 'probe'). The scatterplots are converted into three-dimensional maps that show the propensity of the probe at different positions around the central group. Here we describe how to calculate the similarity of a pair of central groups based on these maps. The similarity method is validated using bioisosteric functional group pairs identified in the Bioster database and Relibase. The Bioster database is a critical compilation of thousands of bioisosteric molecule pairs, including drugs, enzyme inhibitors and agrochemicals. Relibase is an object-oriented database containing structural data about protein-ligand interactions. The distributions of the similarities of the bioisosteric functional group pairs are compared with similarities for all the possible pairs in IsoStar, and are found to be significantly different. Enrichment factors are also calculated showing the similarity method is statistically significantly better than random in predicting bioisosteric functional group pairs.

Artificial Intelligence↗