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Results for “Knowledge base”

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PubMed2004

An approach to knowledge base construction based on expert opinions.

OBJECTIVES: To describe, validate and demonstrate an approach for knowledge base construction based on expert opinions. METHODS: A knowledge base containing the frequency of occurrence of manifestations in epileptic seizures is constructed based on information provided by neurologists/epileptologists. The reliability of the responses is determined with the inter-rater intraclass correlation coefficient (ICC). If the ICC is not large enough the Spearman-Brown prophecy formula can be used to predict the number of additional experts. We propose a method to assess whether an additional expert provides information consistent with the already acquired data as well as a method to detect experts with deviating opinions. The power of the first method was determined. RESULTS: Data were collected for five seizure types. The ICCs determined from the responses for the various seizure types after inclusion of the additional experts was in all cases almost equal to 0.9, the target value. Yet one expert with diverging opinions concerning the frequency of occurrence of manifestations for different seizure types could be identified. Excluding this participant improved the reliability of the data. The power of the methods was good (> or =0.75). CONCLUSIONS: It is shown that human experts can provide reliable information about the frequency of occurrence of manifestations in epileptic seizures. In addition, the described approach correctly identified neurologists/epileptologists with both consistent and diverging opinions about the frequency of occurrence of manifestations in a number of seizure types.

Artificial Intelligence↗

PubMed2002

A situational approach to the design of a patient-oriented disease-specific knowledge base.

We have developed a situational approach to the organization of disease-specific information that seeks to provide patients with targeted access to content in a knowledge base. Our approach focuses on dividing a defined knowledge base into sections corresponding to discrete clinical events associated with the evaluation and treatment of a specific disorder. Common reasons for subspecialty referral are used to generate situational statements that serve as entry points into the knowledge base. Each section includes defining questions generated using keywords associated with specific topics. Defining questions are linked to patient-focused answers. Evaluation of a thyroid cancer web site designed using this approach has identified high ratings for usability, relevance, and comprehension of retrieved information. This approach may be particularly useful in the development of resources for newly diagnosed patients.

Artificial Intelligence↗

PubMed2006

Structural refinement of protein segments containing secondary structure elements: Local sampling, knowledge-based potentials, and clustering.

In this article, we present an iterative, modular optimization (IMO) protocol for the local structure refinement of protein segments containing secondary structure elements (SSEs). The protocol is based on three modules: a torsion-space local sampling algorithm, a knowledge-based potential, and a conformational clustering algorithm. Alternative methods are tested for each module in the protocol. For each segment, random initial conformations were constructed by perturbing the native dihedral angles of loops (and SSEs) of the segment to be refined while keeping the protein body fixed. Two refinement procedures based on molecular mechanics force fields - using either energy minimization or molecular dynamics - were also tested but were found to be less successful than the IMO protocol. We found that DFIRE is a particularly effective knowledge-based potential and that clustering algorithms that are biased by the DFIRE energies improve the overall results. Results were further improved by adding an energy minimization step to the conformations generated with the IMO procedure, suggesting that hybrid strategies that combine both knowledge-based and physical effective energy functions may prove to be particularly effective in future applications.

Algorithms↗

PubMed2001

Knowledge-based computer-aided detection of masses on digitized mammograms: a preliminary assessment.

The purpose of this work was to develop and evaluate a computer-aided detection (CAD) scheme for the improvement of mass identification on digitized mammograms using a knowledge-based approach. Three hundred pathologically verified masses and 300 negative, but suspicious, regions, as initially identified by a rule-based CAD scheme, were randomly selected from a large clinical database for development purposes. In addition, 500 different positive and 500 negative regions were used to test the scheme. This suspicious region pruning scheme includes a learning process to establish a knowledge base that is then used to determine whether a previously identified suspicious region is likely to depict a true mass. This is accomplished by quantitatively characterizing the set of known masses, measuring "similarity" between a suspicious region and a "known" mass, then deriving a composite "likelihood" measure based on all "known" masses to determine the state of the suspicious region. To assess the performance of this method, receiver-operating characteristic (ROC) analyses were employed. Using a leave-one-out validation method with the development set of 600 regions, the knowledge-based CAD scheme achieved an area under the ROC curve of 0.83. Fifty-one percent of the previously identified false-positive regions were eliminated, while maintaining 90% sensitivity. During testing of the 1,000 independent regions, an area under the ROC curve as high as 0.80 was achieved. Knowledge-based approaches can yield a significant reduction in false-positive detections while maintaining reasonable sensitivity. This approach has the potential of improving the performance of other rule-based CAD schemes.

Breast Neoplasms↗

PubMed1988

Early pregnancy disorders: expert knowledge based consultation.

The study presents a part of the knowledge-based consultation system for use in a gynecological primary health service to assist doctors who might not be highly experienced, such as junior gynecologists or general practitioners working in small centers, rural areas, developing countries, etc. Aiding the prompt detection of early pregnancy disorders has been considered to reduce complications in these cases, including the life-threatening ones. Although imminent abortion or ectopic pregnancy is usually assumed, all 17 diagnostic hypotheses manifested through similar symptoms are provided. A special experts' knowledge base including 12,000 rules formulated according to the ELSA-method (Experts Lattice Structured Acquirements) principles has been completed for this purpose. The following pattern serves for obtaining experts acquirements: IF (diagnostic problem) AND (manifestation) THEN (diagnostic hypothesis A) R (diagnostic hypothesis B) where R denotes one of the following relations: ... "preferred rather than"..., ... "preferred strongly to" ..., ... "equivalent to" ... A special procedure has been developed for processing these experts judgements to aid the medical reasoning in complex situations. The consultation system offers its assistance in: --differentiation within any given group of diagnostic hypotheses through setting them in order dependent on the patient's manifestations presented, --selection of the most efficient diagnostic steps for differentiating within the diagnostic hypotheses assumed. A consultation in the case of the patient with irregular vaginal bleeding has been included. The system operates on IBM PC/XT, equipped with 256 RAM, 360 kB floppy and a printer.

Diagnosis, Computer-Assisted↗

PubMed1997

Knowledge based functions for routine use at a German university hospital setting: the issue of fine tuning.

In this paper we present the introduction of knowledge based functions into clinical routine at Giessen University Hospital. For this purpose a therapy planning module at the medical intensive care unit has been extensively redesigned in order to support the structured documentation of drug prescriptions. After introduction of this new HIS component in January 1996 research has been initiated to establish a basic drug therapy knowledge base. The main components of a knowledge based system have been fully incorporated into the hospital information system WING and are in routine use since December 1996. During a pre-production phase warnings of reminder functions were logged and reviewed by an interdisciplinary team in order to adapt the system to the actual clinical environment. The paper describes experiences during this fine tuning and adaptation process which was necessary to bring a small set of knowledge modules into clinical routine.

Artificial Intelligence↗

PubMed1994

An integrated knowledge-based system to guide the physician during structured reporting.

A routinely used system for report generation, based on direct physician data entry, has been combined with a knowledge-based module. The knowledge-based system is operating in the background, and guides the user by dynamically suggesting diagnoses and generating hints regarding the actual and possible further diagnostic procedures. Hybrid technology with neural networks and rules is used. A laboratory evaluation has shown good agreement between diagnoses suggested by the system and confirmed diagnoses, with a kappa coefficient of 0.85 and an area under the ROC curve of 0.92. In a survey following a 14-day test period, physicians stated that the system was both sensible and helpful. The current application domain is ultrasound reporting.

Artificial Intelligence↗

PubMed1993

Evaluating consensus among physicians in medical knowledge base construction.

This study evaluates inter-author variability in knowledge base construction. Seven board-certified internists independently profiled "acute perinephric abscess", using as reference material a set of 109 peer-reviewed articles. Each participant created a list of findings associated with the disease, estimated the predictive value and sensitivity of each finding, and assessed the pertinence of each article for making each judgment. Agreement in finding selection was significantly different from chance: seven, six, and five participants selected the same finding 78.6, 9.8, and 1.6 times more often than predicted by chance. Findings with the highest sensitivity were most likely to be included by all participants. The selection of supporting evidence from the medical literature was significantly related to each physician's agreement with the majority. The study shows that, with appropriate guidance, physicians can reproducibly extract information from the medical literature, and thus established a foundation for multi-author knowledge base construction.

Abscess↗

PubMed2000

Knowledge-based segmentation of thoracic computed tomography images for assessment of split lung function.

The assessment of differential left and right lung function is important for patients under consideration for lung resection procedures such as single lung transplantation. We developed an automated, knowledge-based segmentation algorithm for purposes of deriving functional information from dynamic computed tomography (CT) image data. Median lung attenuation (HU) and area measurements were automatically calculated for each lung from thoracic CT images acquired during a forced expiratory maneuver as indicators of the amount and rate of airflow. The accuracy of these derived measures from fully automated segmentation was validated against those from segmentation using manual editing by an expert observer. A total of 1313 axial images were analyzed from 49 patients. The images were segmented using our knowledge-based system that identifies the chest wall, mediastinum, trachea, large airways and lung parenchyma on CT images. The key components of the system are an anatomical model, an inference engine and image processing routines, and segmentation involves matching objects extracted from the image to anatomical objects described in the model. The segmentation results from all images were inspected by the expert observer. Manual editing was required to correct 183 (13.94%) of the images, and the sensitivity, specificity, and accuracy of the knowledge-based segmentation were greater than 98.55% in classifying pixels as lung or nonlung. There was no significant difference between median lung attenuation or area values from automated and edited segmentations (p > 0.70). Using the knowledge-based segmentation method we can automatically derive indirect quantitative measures of single lung function that cannot be obtained using conventional pulmonary function tests.

Algorithms↗

PubMedDate not supplied

The Section on Medical Expert and Knowledge-Based Systems at the Department of Medical Computer Sciences of the University of Vienna Medical School.

The Section on Medical Expert and Knowledge-Based Systems at the Department of Medical Computer Sciences pursues methodological research in and practical development of knowledge-based computer systems to assist in the decision-making processes for all areas of medical application. Vagueness of medical terms, uncertainty in the co-occurrence of medical entities, and incompleteness in medical theories are well-known characteristics of medical knowledge and ought to be considered in practically-used medical knowledge-based systems. We found that fuzzy set theory and fuzzy logic are powerful theories that model the above-mentioned characteristics. Fuzzy set theory and fuzzy logic were applied in the following systems: CADIAG-II and MedFrame/CADIAG-IV, FuzzyARDS, and FuzzyKBWean. CADIAG-II and MedFrame/CADIAG-IV are framework programs for consultation systems to aid in the differential diagnostic process in internal medicine. FuzzyARDS is an intelligent on-line monitoring program of data from patients with acute respiratory distress syndrome (ARDS) at an intensive care unit (ICU). It employs fuzzy trend detection and fuzzy automata. FuzzyKBWean is an open-loop fuzzy control program for optimization and quality control of the ventilation and weaning process of patients after cardiac surgery at the ICU. The above-mentioned computer systems have reached the state of extensive clinical integration and testing at the Vienna General Hospital. The obtained results show the applicability and usefulness of these systems.

Artificial Intelligence↗

PubMed2002

SMall Molecule Growth 2001 (SMoG2001): an improved knowledge-based scoring function for protein-ligand interactions.

Computational lead design procedures require fast and accurate scoring functions to rank millions of generated virtual ligands for protein targets. In this article, we present an improved version of the SMoG scoring function, called SMoG2001. This function is based on a knowledge-based approach-that is, the free energy parameters are derived from the observed frequencies of atom-atom contacts in the database of three-dimensional structures of protein-ligand complexes via a procedure based on statistical mechanics. We obtained the statistics from the set of 725 complexes. SMoG2001 reproduces the experimental binding constants of the majority of 119 complexes of the testing set with good accuracy. On similar testing sets, SMoG2001 performs better than two other widely used scoring functions, PMF and SCORE1(LUDI), and comparably to DrugScore. SMoG2001 poorly predicts the affinities of ligands interacting via quantum mechanical forces with metal ions and ligands that are large and flexible. We attribute significant improvement in accuracy over previous versions of the SMoG scoring function to a better description of the reference state-that is, the state of no interactions.

Carbonic Anhydrases↗

PubMed2005

Knowledge-based prediction of DNA atomic structure from nucleic sequence.

A simple knowledge-based method for DNA atomic structure prediction from nucleic sequence is presented. We used free B-DNA crystal structures to estimate the distribution of trinucleotide base pairs and tetranucleotide base-pair steps conformational coordinates. We used these distributions as a basis to predict the 3D position of the non-hydrogen atoms of the nucleic bases of any arbitrary DNA sequence of any length. The only constraint imposed was that the structure is a B-DNA one with Watson-Crick complementary base pairs. The method was tested on not seen DNA structures with sequence lengths varying from 6bp to 12bp. The obtained predictions have RMSE around 0.5 A for the translational conformational coordinates, and around 5 degrees for the rotational. For the estimation of the nucleic base non-hydrogen atom coordinates the RMSE is around 1.1 A. The knowledge-based method outperformed a technique based on genetic algorithms in the prediction of B-DNA structures.

Base Sequence↗

PubMed2001

Integration of textual guideline documents with formal guideline knowledge bases.

Numerous approaches have been proposed to integrate the text of guideline documents with guideline-based care systems. Current approaches range from serving marked up guideline text documents to generating advisories using complex guideline knowledge bases. These approaches have integration problems mainly because they tend to rigidly link the knowledge base with text. We are developing a bridge approach that uses an information retrieval technology. The new approach facilitates a versatile decision-support system by using flexible links between the formal structures of the knowledge base and the natural language style of the guideline text.

Artificial Intelligence↗

PubMedDate not supplied

Knowledge based expert systems for medical diagnosis.

Knowledge based expert systems' have been developed in the last decade for many different applications by adopting artificial intelligence techniques. The paper discusses the main characteristics of the expert systems devoted to medical diagnosis (knowledge representation, explanation capability, inexact reasoning) and addresses some of the limitations (mainly system validation and knowledge acquisition). Finally the paper sketches the overall organization of an expert system devoted to the evaluation of liver function.

Artificial Intelligence↗

PubMed1994

Geropsychiatric nursing: a clinical knowledge base in community and institutional settings.

1. The term clinical knowledge base refers to the foundations and areas of knowledge that are integral to an independent and interdependent clinical discipline. 2. The clinical content essential to establish a research base in geropsychiatric nursing includes normal and abnormal aging, nature and magnitude of geriatric mental health problems, diagnostic categories, caregiver and family roles, and cultural determinants of mental health problems and care. 3. The clinical knowledge base for geropsychiatric nursing care in both community and institutional settings consists of the components identified above and the logical integration of each. Integration can be achieved by including these themes in the theoretical and clinical training of nurses.

Clinical Competence↗

PubMed2006

Can Croatia join Europe as competitive knowledge-based society by 2010?

The 21st century has brought important changes in the paradigms of economic development, one of them being a shift toward recognizing knowledge and information as the most valuable commodities of today. The European Union (EU) has been working hard to become the most competitive knowledge-based society in the world, and Croatia, an EU candidate country, has been faced with a similar task. To establish itself as one of the best knowledge-based country in the Eastern European region over the next 4 years, Croatia realized it has to create an education and science system correspondent with European standards and sensitive to labor market needs. For that purpose, the Croatian Ministry of Science, Education, and Sports (MSES) has created and started implementing a complex strategy, consisting of the following key components: the reform of education system in accordance with the Bologna Declaration; stimulation of scientific production by supporting national and international research projects; reversing the "brain drain" into "brain gain" and strengthening the links between science and technology; and informatization of the whole education and science system. In this comprehensive report, we describe the implementation of these measures, whose coordination with the EU goals presents a challenge, as well as an opportunity for Croatia to become a knowledge-based society by 2010.

Croatia↗

PubMed2004

A patterned approach for linking knowledge-based systems to external resources.

Knowledge-based systems (KBSs) have been developed and used in industry and government as assistance systems, voting partner systems, and embedded applications. As web-based systems change the face of software implementations, these closed, internal KBSs need to be integrated into multicomponent applications that provide updated and extensible services. Therefore, KBSs must be adapted to an environment in which data and control are exchanged with external processes and resources; complementing other participating systems or using them to refine its own results. This integration can be a daunting task. If improperly done, it can result in an inefficient and unmanageable composite application. One approach to simplifying this task is the use of architectural patterns for integration. These patterns are assembled from functional entities that resolve component interoperability conflicts. In this paper, we describe an architectural pattern called the Knowledge Director pattern, which directs the integration of a closed KBS into a broader application environment.

Artificial Intelligence↗

PubMed1998

Going beyond information management: using the Comprehensive Accreditation Manual for Hospitals to promote knowledge-based information services.

In 1987, the Joint Commission on Accreditation of Healthcare Organizations (JCAHO) initiated the Agenda for Change, a major revision in the evaluation process for hospitals. An essential component of that change was to shift the emphasis away from standards for individual departments to standards for hospital-wide functions. In recent years, hospital librarians have focused their energy and attention on complying with the standards for the "Management of Information" chapter, specifically the IM.9 section on knowledge-based information. However, the JCAHO has listed the health sciences librarian and library services as having responsibilities in six other chapters within the Comprehensive Accreditation Manual for Hospitals. These chapters can have a major impact on the services of the hospital library for two reasons: (1) they are being read by hospital leaders and other professionals in the organization, and (2) they articulate specific ways to apply knowledge-based information services to the major functions within the hospital. These chapters are "Education"; "Improving Organizational Performance"; "Leadership"; "Management of Human Resources"; "Management of the Environment of Care"; and "Surveillance, Prevention, and Control of Infection." The standards that these chapters promote present specific opportunities for hospital librarians to apply knowledge-based information resources and service to hospital-wide functions. This article reviews these chapters and discusses the standards that relate to knowledge-based information.

Health Promotion↗