PubMed Health⌕ Search

SEARCH · PubMed Health

Results for “Knowledge base”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 127 records · Page 7Linked to original sources
PubMed1998

Patient Dependency Knowledge-Based Systems.

The ability of Patient Dependency Systems to provide information for staffing decisions and budgetary development has been demonstrated. In addition, they have become powerful tools in modern hospital management. This growing interest in Patient Dependency Systems has renewed calls for their automation. As advances in Information Technology and in particular Knowledge-Based Engineering reach new heights, hospitals can no longer afford to ignore the potential benefits obtainable from developing and implementing Patient Dependency Knowledge-Based Systems. Experience has shown that the vast majority of decisions and rules used in the Patient Dependency method are too complex to capture in the form of a traditional programming language. Furthermore, the conventional Patient Dependency Information System automates the simple and rigid bookkeeping functions. On the other hand Knowledge-Based Systems automate complex decision making and judgmental processes and therefore are the appropriate technology for automating the Patient Dependency method. In this paper a new technique to automate Patient Dependency Systems using knowledge processing is presented. In this approach all Patient Dependency factors have been translated into a set of Decision Rules suitable for use in a Knowledge-Based System. The system is capable of providing the decision-maker with a number of scenarios and their possible outcomes. This paper also presents the development of Patient Dependency Knowledge-Based Systems, which can be used in allocating and evaluating resources and nursing staff in hospitals on the basis of patients' needs.

Accounting↗
PubMed2006

M-score: a knowledge-based potential scoring function accounting for protein atom mobility.

A knowledge-based potential scoring function, named M-Score, has been developed based upon 2331 high-resolution crystal structures of protein-ligand complexes. M-Score considers the mobility of protein atoms, describing the location of each protein atom by a Gaussian distribution instead of a fixed position based upon the isotropic B-factors. This leads to an increase in the number of atom-pairs in the construction of knowledge-based potentials and a smoothing effect on the pairwise distribution functions. M-Score was validated using 896 complexes which were not included in the 2331 data set and whose experimentally determined binding affinities were available. The overall linear correlation coefficient (r) between the calculated scores and experimentally determined binding affinities (pKi or pKd) for these 896 complexes is -0.49. Evaluation of M-Score against 17 protein families showed that we obtained good to excellent correlations for six protein families, modest correlations for four protein families, and poor correlations for the remaining seven protein families.

Algorithms↗
PubMed1997

Creating an environment for linking knowledge-based systems to a clinical database: a suite of tools.

A difficulty in using knowledge-based systems has been linking them to clinical databases. The challenge is in making a correct mapping from the data in the knowledge base to the data in the database. At Columbia-Presbyterian Medical Center, we have built a suite of tools developed to create queries that address this challenge. The tools were designed to allow users to easily retrieve data from the database without requiring the users have extensive database and vocabulary knowledge. The tools help users write correct queries (Query Builder), find correct terms in the clinical database (MED Browser), aggregate the resulting data into a useful form (Clinical Database Browser), and allow the user to test the query within the environment of the knowledge-based system (Event Playback). The tools have been in use for one year.

Artificial Intelligence↗
PubMed2001

Developing the knowledge base of family practice.

Borrowed and adapted knowledge is insufficient to optimize the potential of a comprehensive, integrative, relationship-centered generalist approach to improve the health of individuals, families, and communities. The knowledge base for family practice must be expanded by integrating multiple ways of knowing. This involves (1) self-reflective practice by clinicians, (2) involving the patient voice in generating research questions and interpreting data, (3) inquiry into the systems affecting health care, and (4) investigation of disease phenomena and treatment effects in patients over time. A multimethod, transdisciplinary, participatory approach is needed to create knowledge that retains connections with its meaning and context and therefore is readily translated into practice. This research integrates quantitative and qualitative traditions and involves the active participation of both clinicians and patients. The generation of relevant knowledge should be supported through (a) developing a culture of reflective practice among clinicians, (b) expanding the infrastructure for practice-based research, (c) developing a multimethod, transdisciplinary, participatory research paradigm, (d) longitudinal study of the process and outcomes of broad, integrative, relationship-centered care, and (e) incorporating pursuit of new knowledge as a central feature of training programs and policy. The time has come for the generalist disciplines to commit to the generation of new knowledge based on the needs of patients, families, and communities for relationship-centered, integrated, prioritized health care. Development of a culture of learning and inquiry, and the necessary research methods and skills will require a long-term commitment, creation of partnerships, and a focus on core principles by individuals and organizations.

Algorithms↗
PubMedDate not supplied

Integration of knowledge-based system and database for identification of disturbances in fluid and electrolyte balance.

We describe a knowledge-based system which automatically identifies fluid and electrolyte disorders in intensive care patients. The knowledge-based system was built and interfaced to an existing patient data management system (PDMS) in Kuopio University Central Hospital to evaluate the potential of knowledge-based techniques in information management and decision support in the high dependency environment. Because of the integration, the system does not require any manual data input, and it provides a natural extension and increased performance to a current patient data management system used in clinical practise. The paper discusses design considerations and gives the system description. The evaluation of the experimental system in clinical use showed that it performed almost as well as junior clinicians of the intensive care unit.

Artificial Intelligence↗
PubMed1993

Arden Syntax as a standard for knowledge bases in the clinical chemistry laboratory.

Arden Syntax, a standard specification for defining and sharing modular health knowledge bases, is introduced in the clinical chemistry laboratory. A decision support system is constructed and integrated with a laboratory information system for validation of test results with the delta check method. Adopting the Arden Syntax makes it easier to share knowledge bases between laboratories. Tools for handling Arden Syntax knowledge bases and methods for decision support system implementation and integration with a laboratory information system are available today.

Artificial Intelligence↗
PubMed1998

Knowledge-based potentials--back to the roots.

Applications of knowledge-based quantities in protein structure theory are well established but their theoretical foundation, physical interpretation, and range of applicability seems unclear or even controversial. Moreover, the current literature contains terms like "pseudo-energy", "energy-like quantity", or "true energy" which are vague and unclear and terms like "mean-force potential" corresponding to well defined concepts. Seemingly contradictory results are often caused by inconsistent terminology. Often such problems are resolved when the physical nature of the involved quantities is properly defined. We summarize the fundamental principles of mean-force potentials and radial distribution functions as defined in statistical mechanics and put these into perspective with the term "knowledge-based potential".

Databases, Factual↗
PubMed1997

Integration of a knowledge-based system and a clinical documentation system via a data dictionary.

This paper describes the design and realisation of a knowledge-based system and a clinical documentation system linked via a data dictionary. The software was developed as a shell with object oriented methods and C++ for IBM-compatible PC's and WINDOWS 3.1/95. The data dictionary covers terminology and document objects with relations to external classifications. It controls the terminology in the documentation program with form-based entry of clinical documents and in the knowledge-based system with scores and rules. The software was applied to the clinical field of acute abdominal pain by implementing a data dictionary with 580 terminology objects, 501 document objects, and 2136 links; a documentation module with 8 clinical documents and a knowledge-based system with 10 scores and 7 sets of rules.

Abdominal Pain↗
PubMed2004

Novel knowledge-based potentials for ligand docking: variational approach to the old problem.

The variational approach of evaluation for knowledge-based potentials is considered for the first time. In this approach, the problem to derive knowledge-based potentials is solved as the optimization task in the multiparametric model of atom types, reference states and interaction cutoff radii. Using analogy to liquid state theory we offered four new reference states and derived corresponding knowledge-based potentials. The cutoff radii and atom types are optimized to minimize averaged root-mean square deviations (RMSD) of the ligand docked positions regarding to the experimentally determined poses. The number of atom types is varied on the developed atom type tree with 6 root (C, N, O, S, P and the halogen type) and 49 apical atom types. We showed a pronounced effect of atom type choice on docking accuracy and proved that splitting of elements C, N and O of the periodic system up to the 18 optimal atom types essentially improves docking accuracy.

Ligands↗
PubMed1996

Knowledge-based temporal abstraction in clinical domains.

We have defined a knowledge-based framework for the creation of abstract, interval-based concepts from time-stamped clinical data, the knowledge-based temporal-abstraction (KBTA) method. The KBTA method decomposes its task into five subtasks; for each subtask we propose a formal solving mechanism. Our framework emphasizes explicit representation of knowledge required for abstraction of time-oriented clinical data, and facilitates its acquisition, maintenance, reuse and sharing. The RESUME system implements the KBTA method. We tested RESUME in several clinical-monitoring domains, including the domain of monitoring patients who have insulin-dependent diabetes. We acquired from a diabetes-therapy expert diabetes-therapy temporal-abstraction knowledge. Two diabetes-therapy experts (including the first one) created temporal abstractions from about 800 points of diabetic-patients' data. RESUME generated about 80% of the abstractions agreed by both experts; about 97% of the generated abstractions were valid. We discuss the advantages and limitations of the current architecture.

Artificial Intelligence↗
PubMed1993

The effects of comprehensive guidelines for the care of sickle-cell patients in crisis on the nurses' knowledge base and job satisfaction for care given.

The primary purpose of this study was to determine the effects of comprehensive nursing guidelines for sickle-cell patients in crisis on the nurses' knowledge base and job satisfaction for nursing care given. This study was carried out through the use of a single-group pretest-posttest design. A total of 18 subjects participated in the study. At the pretesting, each subject completed an Individual Information Questionnaire for the collection of background data, a Knowledge Base tool and a Job Satisfaction tool. Afterwards, all subjects attended 10.5 hours of educational programmes on sickle-cell disease, treatment, interventions and relaxation training. Following this, the comprehensive guidelines for the care of sickle-cell patients were instituted. At 3 months and 6 months after the guidelines had been in effect, subjects were again asked to complete the Knowledge Base and Job Satisfaction tools. The results of the tools were then measured comparing the three time intervals for any differences in knowledge and job satisfaction using independent measure t-tests. t-Tests were also performed for each question on the Job Satisfaction tool comparing the three time intervals. The results of the analyses demonstrated that there was a statistically significant increase in knowledge between the pretest and 6 month time interval. The findings also showed that there was no statistically significant increase in overall job satisfaction. However, the t-test results from the individual questions demonstrated a statistically significant increase in job satisfaction in the areas of nurse/physician collaboration and having a broad knowledge base of sickle-cell disease.

Acute Disease↗
PubMed1991

A tool for the computer-assisted creation of QMR medical knowledge base disease profiles.

QMR-KAT is a computer-based tool which assists physicians in the construction of the QMR medical knowledge base. Each QMR disease profile results from an in-depth analysis of the published medical literature, and from consultations with expert clinicians. QMR-KAT is an interactive knowledge acquisition program which facilitates the creation of new disease profiles, records the supporting evidence for each disease profile entry, and enforces consistency with the existing knowledge base. The program has been used in the creation of all new QMR disease profiles over the past two years. It has also been used to support a study on the reproducibility of knowledge base construction.

Artificial Intelligence↗
PubMed2000

Comparison of two knowledge bases on the detection of drug-drug interactions.

This paper describes a drug ordering decision support system that helps with the prevention of adverse drug events by detecting drug-drug interactions in drug orders. The architecture of the system was devised in order to facilitate its use attached to physician order entry systems. The described model focuses in issues related to knowledge base maintenance and integration with external systems. Finally, a retrospective study was performed. Two knowledge bases, developed by different academic centers, were used to detect drug-drug interactions in a dataset with 37,237 drug prescriptions. The study concludes that the proposed knowledge base architecture enables content from other knowledge sources to be easily transferred and adapted to its structure. The study also suggests a method that can be used on the evaluation and refinement of the content of drug knowledge bases.

Artificial Intelligence↗
PubMedDate not supplied

A knowledge-based information system for advice in the crisis management of the patient with burns.

A knowledge-based information system that has been designed to be used as an electronic advisor to guide in fluid resuscitation and in the management of the most frequently occurring complications during the first 48 hours after burn injury is described. The system was also developed for training physicians and nurses and may eventually be used for peer review of the management of patients in the burn unit. Ten data screens are used for entry of the administrative data, the clinical background, and the monitored data. The latter include tables for recording fluid therapy and laboratory results. The knowledge base consists of a series of heuristic decision rules that were formulated by a burn care expert and that express the Uppsala fluid resuscitation program to prevent burn shock. The data recorded for a patient are compared with the data in the knowledge base, and the appropriate conclusions are generated. The system's conclusions, the fluid and ventilation prescription, and other required patient management measures are then displayed as a report. The underlying reasoning for each case may be explored by means of the system's explanation facility. The system has been successfully validated by 125 hypothetic cases that represent typical situations of patients with severe burns.

Adult↗
PubMed1991

Changes over time in the knowledge base of practicing internists.

OBJECTIVE: To determine factors affecting the knowledge base of practicing internists. DESIGN: An 82-item multiple-choice examination with questions from the 1988 American Board of Internal Medicine (ABIM) certifying examination was used to assess the knowledge base of 289 internists. SETTING AND PARTICIPANTS: Participants were selected from among practicing internists in New York, New Jersey, and Pennsylvania who had received ABIM certification 5 to 15 years previously. RESULTS: significant inverse correlation (r = -.30) was found between examination scores and the number of years elapsed since certification. Knowledge declined sharply within 15 years of certification. In addition, procedure-oriented subspecialists (cardiologists and gastroenterologists) had lower scores than other internists in this examination of general medical knowledge. Multivariate analyses showed that independent variables that predicted test performance were initial ABIM certifying examination score, time elapsed since certification, subspecialty classification, medical school type, and residency type. CONCLUSIONS: These results support the recent decision for time-limited certification of internists and raise questions related to content and standard setting for recertification examinations.

Certification↗
PubMed2001

Incorporating knowledge-based biases into an energy-based side-chain modeling method: application to comparative modeling of protein structure.

The performance of the self-consistent mean field theory (SCMFT) method for side-chain modeling, employing rotamer energies calculated with the flexible rotamer model (FRM), is evaluated in the context of comparative modeling of protein structure. Predictions were carried out on a test set of 56 model backbones of varying accuracy, to allow side-chain prediction accuracy to be analyzed as a function of backbone accuracy. A progressive decrease in the accuracy of prediction was observed as backbone accuracy decreased. However, even for very low backbone accuracy, prediction was substantially higher than random, indicating that the FRM can, in part, compensate for the errors in the modeled tertiary environment. It was also investigated whether the introduction in the FRM-SCMFT method of knowledge-based biases, derived from a backbone-dependent rotamer library, could enhance its performance. A bias derived from the backbone-dependent rotamer conformations alone did not improve prediction accuracy. However, a bias derived from the backbone-dependent rotamer probabilities improved prediction accuracy considerably. This bias was incorporated through two different strategies. In one (the indirect strategy), rotamer probabilities were used to reject unlikely rotamers a priori, thus restricting prediction by FRM-SCMFT to a subset containing only the most probable rotamers in the library. In the other (the direct strategy), rotamer energies were transformed into pseudo-energies that were added to the average potential energies of the respective rotamers, thereby creating hybrid energy-based/knowledge-based average rotamer energies, which were used by the FRM-SCMFT method for prediction. For all degrees of backbone accuracy, an optimal strength of the knowledge-based bias existed for both strategies for which predictions were more accurate than pure energy-based predictions, and also than pure knowledge-based predictions. Hybrid knowledge-based/energy-based methods were obtained from both strategies and compared with the SCWRL method, a hybrid method based on the same backbone-dependent rotamer library. The accuracy of the indirect method was approximately the same as that of the SCWRL method, but that of the direct method was significantly higher.

Computer Simulation↗
PubMed2002

Towards a sharable numeric and symbolic knowledge base on cerebral cortex anatomy: lessons learned from a prototype.

We propose a knowledge base that combines numeric and symbolic knowledge about sulco-gyral brain cortex. This knowledge base is implemented using Web technologies. It is intended to be easily reusable in various application contexts such as teaching, decision support in neurosurgery and sharing of neuroimaging data for research purposes. Our analysis shows that (1) a formal representation of taxonomy and mereotopology, and (2) use of identity criteria to represent symbolic concepts, are needed to serve those applications.

Artificial Intelligence↗