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Results for “Knowledge base”

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At least 433 records · Page 24Linked to original sources

PubMed1999

[Knowledge-based diagnosis and therapeutic recommendations with fuzzy-set theory methods in patients with acute lung failure (ARDS)].

OBJECTIVE: Since the treatment of patients with severe ARDS using the extracorporal lung assist (ECLA) methods remains a cost intensive and speculative procedure, a knowledge based computer system should be created and evaluated in order to support clinical decisions. METHODS: The model was based on the fuzzy set theory and therefore able to give decisions between yes and no, that means that a criterion could also be fulfilled to 35% or 80% for example. The development of this computer program consists of two steps: first, the entry criteria for the ECLA therapy were established within a framework of an international evaluation of clinical data from 3 centres (Berlin, Marburg, Vienna). Here, inherent vagueness, uncertainty of the occurrence and limited availability of medical data are to be considered to establish a useful tool. Secondly, this was done by grouping and weighting of parameters by the system and the status of each patient or patient group was assigned by the percentage of fulfillment of the criterion. RESULTS: By using a mixed sample of patients from these three centres, the fulfillment of entry criteria according either to definitions of Berlin or to definition of Marburg was different (68% versus 36%). Other differences (36% vs. 22% and 68% vs. 60%) were found between the fuzzy based score and the crisp score which represents the usually performed method. CONCLUSIONS: This now preevaluated minimal data set to describe severe ARDS patients based on the fuzzy set theory may be useful to evaluate patients for ECLA therapy or for another controlled ARDS-therapy.

Acute Disease↗

PubMed1995

Empirical free energy calculations of ligand-protein crystallographic complexes. I. Knowledge-based ligand-protein interaction potentials applied to the prediction of human immunodeficiency virus 1 protease binding affinity.

The steadily increasing number of high-resolution human immunodeficiency virus (HIV) 1 protease complexes has been the impetus for the elaboration of knowledge-based mean field ligand-protein interaction potentials. These potentials have been linked with the hydrophobicity and conformational entropy scales developed originally to explain protein folding and stability. Empirical free energy calculations of a diverse set of HIV-1 protease crystallographic complexes have enabled a detailed analysis of binding thermodynamics. The thermodynamic consequences of conformational changes that HIV-1 protease undergoes upon binding to all inhibitors, and a substantial concomitant loss of conformational entropy by the part of HIV-1 protease that forms the ligand-protein interface, have been examined. The quantitative breakdown of the entropy-driven changes occurring during ligand-protein association, such as the hydrophobic contribution, the conformational entropy term and the entropy loss due to a reduction of rotational and translational degrees of freedom, of a system composed to ligand, protein and crystallographic water molecules at the ligand-protein interface has been carried out. The proposed approach provides reasonable estimates of distinctions in binding affinity and gives an insight into the nature of enthalpyentropy compensation factors detected in the binding process.

Crystallization↗

PubMed1998

A knowledge-based approach to 3-D reconstruction of human cerebral vasculature.

Good visualisation of the vasculature is essential in the diagnosis and treatment of a variety of brain disorders. This paper presents a new approach to 3-D reconstruction of vascular structures using knowledge-based image processing and multimodal image fusion. The task is to reconstruct the human cerebro-vascular system from the partial information collected from a variety of medical imaging instruments and then recombine these limited models into an anatomically accurate one.

Algorithms↗

PubMed2004

Computational chemogenomics approaches to systematic knowledge-based drug discovery.

Chemogenomics, the identification of all possible drugs for all possible targets, has recently emerged as a new paradigm in drug discovery in which efficiency in the compound design and optimization process is achieved through the gain and reuse of targeted knowledge. As targeted knowledge resides at the interface between chemistry and biology, computational tools aimed at integrating the chemical and biological spaces play a central role in chemogenomics. This review covers the recent progress made in integrative computational approaches to data annotation and knowledge generation for the systematic knowledge-based design and screening of chemical libraries.

Chemistry, Pharmaceutical↗

PubMed1995

HEPAXPERT-III: knowledge-based interpretation of serologic tests for hepatitis A, B, C, and D.

1. The HEPAXPERT-III SYSTEM. HEPAXPERT-III--the successor of HEPAXPERT-I[1] and HEPAXPERT-II [2]--is a routinely-used, integrated medical database and knowledge-based system that stores and interprets the results of serologic tests for infection with hepatitis A, B, C, and D viruses. The following tests are included: Anti-HAV, IGM anti-HAV, and HAV in stool; HBsAg, anti-HBs, anti-HBc, IGM anti-HBc, HBeAg, anti-HBe, and anti-HBs titre; Anti-HCV, HCV-immunoblot, and HCV-PCR; Delta-Ag and anti-delta. HEPAXPERT-III provides the following functions: a) screen input of patient's personal data (patient ID, surname, first name, name at birth, date of birth, and sex), administrative data (department requiring the tests and date of specimen sample), and medical data (results of serologic tests); and/or b) automatic transfer of patient's personal, administrative, and medical data by connecting HEPAXPERT-III to a laboratory information system, a hospital information system, or an automated laboratory analyzer; and c) automatic generation of interpretive reports of the obtained serologic findings, including an analysis of possible virus exposition, immunity, disease stage, prognosis, and degree of infectiousness. HEPAXPERT-I and HEPAXPERT-II have been routinely used at the Vienna General Hospital, the teaching hospital of the University of Vienna Medical School. The interpretive reports are well-accepted and lead to several improvements in patient care [3]. HEPAXPERT-III will not only extend the scope of interpretation to hepatitis C and D serologic tests, but will also offer a state-of-the-art graphical user interface. 2. HARDWARE AND SOFTWARE. IBM-compatible personal computer (minimum 80386 SX processor), 8 MB RAM (OS/2) and 4 RM RAM (MS-Windows), resp., graphic adapter (minimum 640x480) and printer supported by OS/2 and MS-Windows 3.1, resp., IBM OS/2-2.1 or higher and MS-Windows 3.1 or higher, resp., and for the OS/2 version IBM Database 2 (DB2/2).

Diagnosis, Computer-Assisted↗

PubMed2005

Knowledge-based voting algorithm for automated protein functional annotation.

Automated annotation of high-throughput genome sequences is one of the earliest steps toward a comprehensive understanding of the dynamic behavior of living organisms. However, the step is often error-prone because of its underlying algorithms, which rely mainly on a simple similarity analysis, and lack of guidance from biological rules. We present herein a knowledge-based protein annotation algorithm. Our objectives are to reduce errors and to improve annotation confidences. This algorithm consists of two major components: a knowledge system, called "RuleMiner," and a voting procedure. The knowledge system, which includes biological rules and functional profiles for each function, provides a platform for seamless integration of multiple sequence analysis tools and guidance for function annotation. The voting procedure, which relies on the knowledge system, is designed to make (possibly) unbiased judgments in functional assignments among complicated, sometimes conflicting, information. We have applied this algorithm to 10 prokaryotic bacterial genomes and observed a significant improvement in annotation confidences. We also discuss the current limitations of the algorithm and the potential for future improvement.

Algorithms↗

PubMed1994

Development of Design-a-Trial, a knowledge-based critiquing system for authors of clinical trial protocols.

Many published clinical trials are poorly designed, suggesting that the protocol was incomplete, disorganised or contained errors. This fact, doctors' limited statistical skills and the shortage of medical statisticians, prompted us to develop a knowledge-based aid, Design-a-Trial, for authors of clinical trial protocols. This interviews a physician, prompts them with suitable design options, comments on the statistical rigour and feasibility of their proposed design and generates a 6-page draft protocol document. This paper outlines the process used to develop Design-a-Trial, presents preliminary evaluation results, and discusses lessons we learned which may apply to the developed of other medical decision-aids.

Clinical Trials as Topic↗

PubMed1990

Surgical knowledge base augmentation by medical rotations: does it happen?

A study of 4 groups of fifth year medical students taking Surgery during the 4 terms of 1989 at the University of Queensland was undertaken to determine whether there was assimilation of factual material relevant to the surgical knowledge base from the other specialty rotations done during the same year of the course. The records of multiple choice question (MCQ) examination results for the 210 students were retrieved and reviewed. The performance of the same students during their fourth-year rotation in Surgery was checked to make sure that the 4 groups did not already display unusual surgical aptitude or incompetence. The questions were categorized in order to ascertain that the content of all the examinations was similar. The results of students doing Surgery during the first of the 4 terms in 1989 were compared with subsequent groups. The difference between the groups was that those in the first term had not had the benefit of fifth year rotations through Internal Medicine, Psychiatry, General Practice and electives. Subsequent groups had increasing experience in the other specialties. The fourth and final group in the year had undertaken all four of the other rotations before doing Surgery. Significant improvement was found in the performance of each of the subsequent groups of students compared with the first-term group. This implies that there is an escalating accural of factual knowledge related to surgery from the fifth-year courses in Internal Medicine, Child Health and Psychiatry.

Curriculum↗

PubMed1992

Knowledge-based decision support for patient monitoring in cardioanesthesia.

An approach to generating 'intelligent alarms' is presented that aggregates many information items, i.e. measured vital signs, recent medications, etc., into state variables that more directly reflect the patient's physiological state. Based on these state variables the described decision support system AES-2 also provides therapy recommendations. The assessment of the state variables and the generation of therapeutic advice follow a knowledge-based approach. Aspects of uncertainty, e.g. a gradual transition between 'normal' and 'below normal', are considered applying a fuzzy set approach. Special emphasis is laid on the ergonomic design of the user interface, which is based on color graphics and finger touch input on the screen. Certain simulation techniques considerably support the design process of AES-2 as is demonstrated with a typical example from cardioanesthesia.

Anesthesia↗

PubMed1994

Knowledge-based cephalometric analysis: a comparison with clinicians using interactive computer methods.

In modern orthodontic practice great reliance is placed on systematic and objective methods of characterizing craniofacial forms, using measurements based on both hard and soft tissue landmarks. Lateral skull X-ray images are routinely used in cephalometric analysis to provide quantitative measurements useful to clinical orthodontists. It is argued that a model- and knowledge-based methodology provides the best approach in successfully interpreting digitized lateral skull radiographs. A rule-based segmentation system, making use of an image appearance model, is used to extract image features from gray-level images. Complex image features and cephalometric landmarks are constructed from these segmented component features. A predictive model, defining picture structure, allows location hypotheses to be made for image features. The underlaying structure of the location model provides the basis for a geometric constraint model of use in discriminating between image feature candidates. A blackboard system is used to organize these tasks hierarchically, with individual knowledge sources grouped according to function and the individual stages of the adopted image interpretation cycle. Quantitative results demonstrate the superiority of this complex system over its component segmentation system run on its own. Comparisons with clinicians demonstrate both the strengths and the weaknesses of the present system. Comparisons with previous systems are favorable.

Cephalometry↗

PubMed1999

A knowledge-based patient image prefetching system: design, evaluation and management.

One fundamental clinical role of radiologists is to provide attending physicians with interpretations of an individual patient's radiological images essential to a treatment plan or overall patient management. Interpreting images from a newly taken radiological examination often requires reference to prior images of the same patient to establish a baseline from which to confirm a suspected pathological process or injury or to evaluate the progression of one that has been identified. Such image references are crucial to the radiologist's examination reading and when inappropriately supported can result in prolonged reading time, decreased report quality, and frustration. To address the problem of inadequate image prefetching methods used by many health care organizations, we took a knowledge-based approach and developed Image Retrieval Expert System (IRES), which incorporates relevant medical/radiological knowledge and contains image retrieval heuristics commonly shared by radiologists. This article describes the design of IRES, highlights its preliminary evaluation results, and discusses issues important for managing this and similar technologies in a health care organization.

Artificial Intelligence↗

PubMed2004

Processes and problems in the formative evaluation of an interface to the Foundational Model of Anatomy knowledge base.

The Digital Anatomist Foundational Model of Anatomy (FMA) is a large semantic network of more than 100,000 terms that refer to the anatomical entities, which together with 1.6 million structural relationships symbolically represent the physical organization of the human body. Evaluation of such a large knowledge base by domain experts is challenging because of the sheer size of the resource and the need to evaluate not just classes but also relationships. To meet this challenge, the authors have developed a relation-centric query interface, called Emily, that is able to query the entire range of classes and relationships in the FMA, yet is simple to use by a domain expert. Formative evaluation of this interface considered the ability of Emily to formulate queries based on standard anatomy examination questions, as well as the processing speed of the query engine. Results show that Emily is able to express 90% of the examination questions submitted to it and that processing time is generally 1 second or less, but can be much longer for complex queries. These results suggest that Emily will be a very useful tool, not only for evaluating the FMA, but also for querying and evaluating other large semantic networks.

Anatomy↗

PubMed1999

The genexpress IMAGE knowledge base of the human muscle transcriptome: a resource of structural, functional, and positional candidate genes for muscle physiology and pathologies.

Sequence, gene mapping, and expression data corresponding to 910 genes transcribed in human skeletal muscle have been integrated to form the muscle module of the Genexpress IMAGE Knowledge Base. Based on cDNA array hybridization, a set of 14 transcripts preferentially or specifically expressed in muscle have been selected and characterized in more detail: Their pattern of expression was confirmed by Northern blot analysis; their structure was further characterized by full-insert cDNA sequencing and cDNA extension; the map location of the corresponding genes was refined by radiation hybrid mapping. Five of the 14 selected genes appear as interesting positional and functional candidate genes to study in relation with muscle physiology and/or specific orphan muscular pathologies. One example is discussed in more detail. The expression profiling data and the associated Genexpress Index2 entries for the 910 genes and the detailed characterization of the 14 selected transcripts are available from a dedicated Web server at. The database has been organized to provide the users with a working space where they can find curated, annotated, integrated data for their genes of interest. Different navigation routes to exploit the resource are discussed.

Base Sequence↗

PubMed1992

Augmented transition networks as a representation for knowledge-based history-taking systems.

Numerous history-taking systems have been built to automate the medical history-taking process. These systems differ in their control methods, input and output modalities, and kinds of questions asked. Thus, there has emerged no standard way of representing interviewing knowledge--the expert knowledge used to govern the sequence of questions asked in an interview. This paper discusses how we use an augmented transition network (ATN) to represent the knowledge of a speech-driven automated history-taking program, Q-MED, and how, more generally, ATNs could be used as a representation for any knowledge-based history-taking system. We identify three characteristics of ATNs that facilitate the use of ATNs in interviewing systems: explicitness, hierarchical structure, and generality.

Artificial Intelligence↗

PubMed2003

Hypertension in people with Type 2 diabetes: knowledge-based diabetes-specific guidelines.

The International Diabetes Federation (Europe) has updated these guidelines on hypertension management specifically in Type 2 diabetes in the light of recent results of the first prospective, randomized controlled studies to investigate clinical outcomes in people with diabetes and hypertension. The guidelines are knowledge based, i.e. based not only on evidence originating from clinical trials, but also from epidemiological and pathophysiological studies. A successful management strategy requires the following components: 1. Regular surveillance to detect developing hypertension and other cardiovascular (CV) risk factors. 2. Considering more frequent monitoring and review of CV risk factors if any single blood pressure (BP) measurement > 140/85 mmHg (or 130/75 if microalbuminuria); when appropriate, using ambulatory or home monitoring to establish the baseline BP. 3. Considering other CV risk factors, such as a raised albumin excretion rate, in setting the intervention threshold. 4. Individualizing the target BP in accordance with other CV risk factors. 5. Agreeing lifestyle and therapeutic interventions with the patient, with education and empowerment as required. 6. Implementing lifestyle modifications, including controlling calorie, salt and alcohol intake, increased physical activity, weight control and smoking cessation. 7. Therapeutic strategy: the primary goal of therapy is to reduce BP markedly. Combination therapy is often necessary, e.g. an angiotensin converting enzyme (ACE) inhibitor and a diuretic. Some classes are particularly useful for certain patients, notably longer-acting ACE inhibitors, angiotensin 2 receptor antagonists (A2RAs) and calcium antagonists in those at risk of diabetic nephropathy, loop diuretics and thiazides in those at risk of hyperkalaemia, beta-blockers and calcium antagonists (except short-acting dihydropyridines) in patients with angina, beta-blockers and ACE inhibitors after a myocardial infarction or in those with left ventricular dysfunction, and thiazide diuretics and long-acting dihydropyridine calcium antagonists for isolated systolic hypertension. A2RAs should be particularly considered when ACE inhibitors are not tolerated. alpha 1-Blockers should not be considered first line in the absence of outcome data. Cost of drugs will modify these strategies in developing countries. 8. Monitoring response to therapies and, if target levels are not achieved, either intensifying drug therapy if the CV risk justifies it, or reassessing the target. 9. Maintaining a quality assurance strategy. This strategy is summarized in a simple, practical management algorithm.

Antihypertensive Agents↗

PubMed2002

Army nurses' knowledge base for determining triage categories in a mass casualty.

The timing, location, and participants in a mass casualty scenario cannot be predicted. Nurses may be involved in performing triage, yet there is no published documentation of military nurses' ability to triage. A prospective design was used to describe 82 Army nurses' knowledge base related to designating triage categories for patients during a mass causality, examining the relationships among their education and experience as evaluated by The Darnall Mass Casualty Triage Test and Demographic Data Form. The most significant areas associated with higher scores on the Triage Test were: completion of Advanced Cardiac Life Support, advanced certification as a Certified Registered Nurse Anesthetists, Certified Emergency Nurse, or Critical Care Registered Nurse, and attendance to the Medical Management of Nuclear Weapons Course. An improved average score for nurses overall was also noted when compared with previous work with the Darnall MASCAL Triage Test.

Analysis of Variance↗

PubMed2001

A knowledge-based, concept-oriented view generation system for clinical data.

Information overload is a well-known problem for clinicians who must review large amounts of data in patient records. Concept-oriented views, which organize patient data around clinical concepts such as diagnostic strategies and therapeutic goals, may offer a solution to the problem of information overload. However, although concept-oriented views are desirable, they are difficult to create and maintain. We have developed a general-purpose, knowledge-based approach to the generation of concept-oriented views and have developed a system to test our approach. The system creates concept-oriented views through automated identification of relevant patient data. The knowledge in the system is represented by both a semantic network and rules. The key relevant data identification function is accomplished by a rule-based traversal of the semantic network. This paper focuses on the design and implementation of the system; an evaluation of the system is reported separately.

Artificial Intelligence↗

PubMed2000

Development of a knowledge-base for automatic monitoring of renal function of intensive care patients over time.

Renal dysfunction is a major problem in the management of critically ill patients. Monitoring of renal parameters over time is a prerequisite for detection of any significant deterioration of kidney function. Thus, we developed a knowledge-base for the dynamic monitoring of renal function of critically ill patients. A database with renal parameters of 750 intensive care patients was analyzed for distribution of parameters within predefined intervals of the creatinine clearance. Additionally, a subgroup of 11 patients with (quite) normal renal function over 11 days was selected and the daily variability of renal parameters was analyzed. An interdisciplinary expert team selected a set of nine clinically relevant renal parameters and formulated, on the basis of the data analysis and the parameter set, eight definitions of renal function, which represent four levels of renal performance. These definitions were arranged into an hierarchical structure, considering only clinically relevant changes of renal function. A change from one functional state to another inside of 2 days indicates a relevant alteration of renal function. Monitoring of time courses can additionally be performed by statistical analysis of the daily variability of parameters and comparison with their 'normal' variability. Moreover, rules were established for the plausibility check of results and interpretations of single parameters and parameter sets formulated.

Artificial Intelligence↗