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PubMed1990

Knowledge-based representations of risk beliefs.

Beliefs about risks associated with two risk agents, AIDS and toxic waste, are modeled using knowledge-based methods and elicited from subjects via interactive computer technology. A concept net is developed to organize subject responses concerning the consequences of the risk agents. It is found that death and adverse personal emotional and sociological consequences are most associated with AIDS. Toxic waste is most associated with environmental problems. These consequence profiles are quite dissimilar, although past work in risk perception would have judged the risk agents as being quite similar. Subjects frequently used causal semantics to represent their beliefs and "% of time" instead of "probability" to represent likelihoods. The news media is the most prevalent source of risk information although experiences of acquaintances appear more credible. The results suggest that "broadly based risk" communication may be ineffective because people differ in their conceptual representation of risk beliefs. In general, the knowledge-based approach to risk perception representation has great potential to increase our understanding of important risk topics.

Acquired Immunodeficiency Syndrome↗

PubMed1995

A clinical trial of a knowledge-based medical record.

To meet the needs of primary care physicians caring for patients with HIV infection, we developed a knowledge-based medical record to allow the on-line patient record to play an active role in the care process. These programs integrate the on-line patient record, rule-based decision support, and full-text information retrieval into a clinical workstation for the practicing clinician. To determine whether use of a knowledge-based medical record was associated with more rapid and complete adherence to practice guidelines and improved quality of care, we performed a controlled clinical trial among physicians and nurse practitioners caring for 349 patients infected with the human immuno-deficiency virus (HIV); 191 patients were treated by 65 physicians and nurse practitioners assigned to the intervention group, and 158 patients were treated by 61 physicians and nurse practitioners assigned to the control group. During the 18-month study period, the computer generated 303 alerts in the intervention group and 388 in the control group. The median response time of clinicians to these alerts was 11 days in the intervention group and 52 days in the control group (PJJ0.0001, log-rank test). During the study, the computer generated 432 primary care reminders for the intervention group and 360 reminders for the control group. The median response time of clinicians to these alerts was 114 days in the intervention group and more than 500 days in the control group (PJJ0.0001, log-rank test). Of the 191 patients in the intervention group, 67 (35%) had one or more hospitalizations, compared with 70 (44%) of the 158 patients in the control group (PJ=J0.04, Wilcoxon test stratified for initial CD4 count). There was no difference in survival between the intervention and control groups (P = 0.18, log-rank test). We conclude that our clinical workstation significantly changed physicians' behavior in terms of their response to alerts regarding primary care interventions and that these interventions have led to fewer patients with HIV infection being admitted to the hospital.

AIDS-Related Opportunistic Infections↗

PubMed1991

Weaning patients from mechanical ventilation. A knowledge-based system approach.

The WEANing PROtocol (WEANPRO) knowledge-based system assists respiratory therapists and nurses in weaning post-operative cardiovascular patients from mechanical ventilation in the intensive care unit. The knowledge contained in WEANPRO is represented by rules and is implemented in M.1 by Teknowledge, Inc. WEANPRO will run on any IBM-compatible microcomputer. WEANPRO's performance in weaning patients in the intensive care unit was evaluated three ways: (1) a statistical comparison between the mean number of arterial blood gases required to wean patients to a T-piece with and without the use of WEANPRO, (2) a critique of the suggestions offered by the system by clinicians not involved in the system development, and (3) an inspection of the users acceptance of WEANPRO in the intensive care unit. The results of the evaluations revealed that using WEANPRO significantly decreases the number of arterial blood gas analyses needed to wean patients from total dependance on mechanical ventilation to independent breathing using a T-piece. In doing so, WEANPRO's suggestions are accurate and its use is accepted by the clinicians. Currently, WEANPRO is being used in the intensive care unit at the East Unit of Baptist Memorial Hospital in Memphis, Tennessee.

Artificial Intelligence↗

PubMed1996

Computer-aided diagnosis in jaundice: comparison of knowledge-based and probabilistic approaches.

The study reported in this paper is aimed at evaluating the effectiveness of a knowledge-based expert system (ICTERUS) in diagnosing jaundiced patients, compared with a statistical system based on probabilistic concepts (TRIAL). The performances of both systems have been evaluated using the same set of data in the same number of patients. Both systems are spin-off products of the European project Euricterus, an EC-COMAC-BME Project designed to document the occurrence and diagnostic value of clinical findings in the clinical presentation of jaundice in Europe, and have been developed as decision-making tools for the identification of the cause of jaundice based only on clinical information and routine investigations. Two groups of jaundiced patients were studied, including 500 (retrospective sample) and 100 (prospective sample) subjects, respectively. All patients were independently submitted to both decision-support tools. The input of both systems was the data set agreed within the Euricterus Project. The performances of both systems were evaluated with respect to the reference diagnoses provided by experts on the basis of the full clinical documentation. Results indicate that both systems are clinically reliable, although the diagnostic prediction provided by the knowledge-based approach is slightly better.

Artificial Intelligence↗

PubMed1989

The knowledge base of certified internists. Relationships to training, practice type, and other physician characteristics.

A written examination was used to assess the knowledge base of 183 practicing certified internists. Analyses of the examination scores showed that performance on the initial American Board of Internal Medicine certification examination taken 7.6 years previously was the major factor predicting current knowledge base. By developing regression models, the unique contribution of different variables to prediction of current examination scores was determined. Prior American Board of Internal Medicine certification examination performance accounted for 70.9% of the explained variance, and demographic and practice variables were responsible for 17.8%. Among the demographic and practice variables studied, community size and subspecialty practice were the only variables that contributed significantly to the regression equations. Examination scores were highest for certified internists practicing in smaller communities. General internists received higher scores than subspecialists. Although statistically significant, the apparent adverse influence of subspecialty practice and larger community size on examination performance was modest. Further study is needed to determine if longer periods in practice might produce different relationships between variables such as these and examination performance.

Certification↗

PubMed2002

Knowledge-based assessment of gene expression data from chemiluminescence detection.

The first problem in gene expression profiling to be solved is choosing the appropriate gene array, detection procedure, image analysis and data generation depending on the organism of interest, equipment and budget. The next one is how to deduce biologically meaningful data. We assessed gene expression data from chemiluminescent detection and empirically found criteria for the reliable identification of biologically meaningful expression ratios. Current statistical assessments are often applied unreflectedly concerning problems occurring in practice. So interesting results are considered to be irrelevant. This requires a laborious data check. We suggest automation. Our empirically found criteria were transformed into and validated by a knowledge-based system. This system is adaptable to all other methods of expression profiling. We compared the experience-based and new knowledge-based assessment of the expression data from our chemiluminescent and additionally radioactive detection of several experiments with published data to evaluate our entire procedure. With our adaptation of chemiluminescence detection to commercially available Escherichia coli gene arrays we present a useful alternative to common procedures in gene expression monitoring. Moreover, with our consideration of plasmid-harbouring E. coli strains we provide the opportunity to monitor gene expression during processes requiring any plasmids (e.g. recombinant protein expression).

Artificial Intelligence↗

PubMed1992

Development of the knowledge-based standard for the written certification examination of the American Board of Anesthesiology.

In 1988 and 1989, the American Board of Anesthesiology (ABA) developed a knowledge-based standard for its written certification examination. In brief, 13 "judges" developed a construct of a "borderline candidate," i.e., a candidate who was neither ideal nor clearly failing but rather had sufficient knowledge to just pass. In 1989, this construct was applied to 90 questions from the 1989 ABA examination to estimate candidate's score on that subset. When extended to the entire examination, the use of the construct resulted in a knowledge-based standard of 57% correct. (The 1988 exercise, also using the construct of a borderline candidate but with a totally different subset of questions, produced an identical standard). This standard resulted in higher success rates among the actual examinees taking the ABA examination (84% in 1989 and 90% in 1990) than had the normative standard used previously (80%). The authors suggest that the process they describe permits development of a reproducible criterion for success that is based entirely on mastery of a relevant body of knowledge rather than on normative considerations.

Anesthesiology↗

PubMedDate not supplied

An architecture for knowledge-based construction of decision models.

Clinical application of decision analysis has been limited by unfamiliarity of clinicians with the technique, large data requirements, and the length of time needed to construct models. In order to make decision modeling more accessible to clinicians, the authors developed a computer program to construct decision models automatically. The system contains two separate knowledge bases. One contains frames encoding knowledge of the medical domain, the evaluation of pulmonary disease in patients infected with the human immunodeficiency virus (HIV). The other contains rules of correct decision model construction that guide the selection of items from the domain knowledge base and their insertion into the decision model. The system can create either a tree or an influence diagram that satisfies previously published critiquing rules. The system has the potential to enable novices to construct useful decision models and to provide individualized decision-analytic advice to clinicians in real time.

AIDS-Related Opportunistic Infections↗

PubMed2000

Analysis of knowledge-based protein-ligand potentials using a self-consistent method.

We propose a self-consistent approach to analyze knowledge-based atom-atom potentials used to calculate protein-ligand binding energies. Ligands complexed to actual protein structures were first built using the SMoG growth procedure (DeWitte & Shakhnovich, 1996) with a chosen input potential. These model protein-ligand complexes were used to construct databases from which knowledge-based protein-ligand potentials were derived. We then tested several different modifications to such potentials and evaluated their performance on their ability to reconstruct the input potential using the statistical information available from a database composed of model complexes. Our data indicate that the most significant improvement resulted from properly accounting for the following key issues when estimating the reference state: (1) the presence of significant nonenergetic effects that influence the contact frequencies and (2) the presence of correlations in contact patterns due to chemical structure. The most successful procedure was applied to derive an atom-atom potential for real protein-ligand complexes. Despite the simplicity of the model (pairwise contact potential with a single interaction distance), the derived binding free energies showed a statistically significant correlation (approximately 0.65) with experimental binding scores for a diverse set of complexes.

Ligands↗

PubMed2006

A knowledge-based scoring function based on residue triplets for protein structure prediction.

One of the general paradigms for ab initio protein structure prediction involves sampling the conformational space such that a large set of decoy (candidate) structures are generated and then selecting native-like conformations from those decoys using various scoring functions. In this study, based on a physical/geometric approach first suggested by Banavar and colleagues, we formulate a knowledge-based scoring function, which uses the radii of curvature formed among triplets of residues in a protein conformation. By analyzing its performance on various decoy sets, we determine a good set of parameters--the distance cutoff and the number of distance bins--to use for configuring such a function. Furthermore, we investigate the effect of using various approaches for compiling the prior distribution on the performance of the knowledge-based function. Possible extensions to the current form of the residue triplet scoring function are discussed.

Algorithms↗

PubMed2006

An iterative knowledge-based scoring function to predict protein-ligand interactions: II. Validation of the scoring function.

We have developed an iterative knowledge-based scoring function (ITScore) to describe protein-ligand interactions. Here, we assess ITScore through extensive tests on native structure identification, binding affinity prediction, and virtual database screening. Specifically, ITScore was first applied to a test set of 100 protein-ligand complexes constructed by Wang et al. (J Med Chem 2003, 46, 2287), and compared with 14 other scoring functions. The results show that ITScore yielded a high success rate of 82% on identifying native-like binding modes under the criterion of rmsd < or = 2 A for each top-ranked ligand conformation. The success rate increased to 98% if the top five conformations were considered for each ligand. In the case of binding affinity prediction, ITScore also obtained a good correlation for this test set (R = 0.65). Next, ITScore was used to predict binding affinities of a second diverse test set of 77 protein-ligand complexes prepared by Muegge and Martin (J Med Chem 1999, 42, 791), and compared with four other widely used knowledge-based scoring functions. ITScore yielded a high correlation of R2 = 0.65 (or R = 0.81) in the affinity prediction. Finally, enrichment tests were performed with ITScore against four target proteins using the compound databases constructed by Jacobsson et al. (J Med Chem 2003, 46, 5781). The results were compared with those of eight other scoring functions. ITScore yielded high enrichments in all four database screening tests. ITScore can be easily combined with the existing docking programs for the use of structure-based drug design.

Acetylcholinesterase↗

PubMed2006

An iterative knowledge-based scoring function to predict protein-ligand interactions: I. Derivation of interaction potentials.

Using a novel iterative method, we have developed a knowledge-based scoring function (ITScore) to predict protein-ligand interactions. The pair potentials for ITScore were derived from a training set of 786 protein-ligand complex structures in the Protein Data Bank. Twenty-six atom types were used based on the atom type category of the SYBYL software. The iterative method circumvents the long-standing reference state problem in the derivation of knowledge-based scoring functions. The basic idea is to improve pair potentials by iteration until they correctly discriminate experimentally determined binding modes from decoy ligand poses for the ligand-protein complexes in the training set. The iterative method is efficient and normally converges within 20 iterative steps. The scoring function based on the derived potentials was tested on a diverse set of 140 protein-ligand complexes for affinity prediction, yielding a high correlation coefficient of 0.74. Because ITScore uses SYBYL-defined atom types, this scoring function is easy to use for molecular files prepared by SYBYL or converted by software such as BABEL.

Ligands↗

PubMed2005

Classification and comparison of ligand-binding sites derived from grid-mapped knowledge-based potentials.

We describe the application of knowledge-based potentials implemented in the MOE program to compare the ligand-binding sites of several proteins. The binding probabilities for a polar and a hydrophobic probe are calculated on a grid to allow easy comparison of binding sites of superimposed related proteins. The method is fast and simple enough to simultaneously use structural information of multiple proteins of a target family. The method can be used to rapidly cluster proteins into subfamilies according to the similarity of hydrophobic and polar fields of their ligand-binding sites. Regions of the binding site which are common within a protein family can be identified and analysed for the design of family-targeted libraries or those which differ for improvement of ligand selectivity. The field-based hierarchical clustering is demonstrated for three protein families: the ligand-binding domains of nuclear receptors, the ATP-binding sites of protein kinases and the substrate binding sites of proteases. More detailed comparisons are presented for serine proteases of the chymotrypsin family, for the peroxisome proliferator-activated receptor subfamily of nuclear receptors and for progesterone and androgen receptor. The results are in good accordance with structure-based analysis and highlight important differences of the binding sites, which have been also described in the literature.

Binding Sites↗

PubMed1998

Structural-functional bioinformatics: knowledge-based NMR interpretation.

This paper describes a knowledge-based approach to a problem of structural-functional bioinformatics, specifically the determination of protein structure through the automated analysis of NMR data. Highly successful results in carrying out sequence-specific assignments of residues from multidimensional NMR datasets has led us to automation of NOE dataset interpretation and a design for integrating these results with other protein structure and function analysis programs.

Computational Biology↗

PubMed1993

Knowledge-based approach to clinical decision-support system, with an application in tetanus serology.

In tetanus immunization, the need for a booster vaccination can be easily determined by a serological analysis of tetanus antitoxin. Vaccination without knowledge of the immune status carries a high risk of postvaccinal complications. We have used the Pro. M.D. expert system shell to produce the knowledge base TETANUS, to improve the diagnostic interpretation of tetanus antitoxin findings. This knowledge base is comprehensive, runnable, useful, and modifiable by anyone who works with Pro.M.D. and is able to provide medical knowledge in this field.

Antitoxins↗

PubMed2002

Using LOINC to link an EMR to the pertinent paragraph in a structured reference knowledge base.

Intermountain Health Care has integrated the electronic medical record (EMR) with online information resources in order to create easy access to a knowledge base which practicing physicians can use at the point of care. When a user is reviewing problems/diagnosis, medications, or clinical laboratory test results, they can conveniently access a "pertinent paragraph" of reference literature that pertains to the clinical data in the EMR. Using terminology first coined by Cimino1, we call this application the "infobutton." We describe the architectural issues involved in linking our electronic medical record with a structured laboratory knowledge base. The application has been well received as noted by anecdotal comments made by physicians and usage of the application.

Clinical Laboratory Techniques↗

PubMedDate not supplied

Working memory, intelligence and knowledge base in adult persons with intellectual disability.

Previous studies have suggested that performance in working memory (WM) tasks is deficient in all etiologies and at all levels of intellectual disability (ID). Knowledge about WM structure, cognitive processes reflected in WM tasks, or the long-term memory contribution to WM capacity in ID is. however, not satisfactory. In the present study, WM capacity, WM task requirements, as well as effects between WM, skills, knowledge base, and intelligence were explored in two groups with matched fluid intelligence: adult persons with ID and normally developing children aged 3-6 years. The ID Group performed equally well as the children in WM tasks based on familiar semantic information and were significantly better on all measures reflecting skills and knowledge base. The Child Group performed better in phonological and visuo-spatial WM tasks including nonsemantic information, respectively. In particular, it appeared that the groups differed in their WM performance although they were matched for fluid intelligence. We hypothesize that the ID Group depended more on knowledge support from long-terrm memory whereas the Child Group could benefit more from efficient online WM processes.

Cognition Disorders↗

PubMed2006

Knowledge-based chemoinformatic approaches to drug discovery.

The modern drug discovery process is steadily becoming more information driven. Structural, physicochemical and ADME-Tox property profiles of reference (successful) ligands, along with structural information of their target proteins, have been extremely useful for early-stage drug discovery. Recently, databases of known biologically active ligands (knowledge bases) have become more focused toward different protein-target classes. The number of new chemoinformatics tools used to analyze structures and properties of successful molecules has also increased enormously. Scientists in this area are exploring new physicochemical properties and appropriate drug sets to understand druglike properties. In this review, the various uses of the ligand knowledge bases in the drug discovery process have been critically reviewed.

Drug Design↗