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PMF scoring revisited.

Knowledge-based scoring functions have become accepted choices for fast scoring putative protein-ligand complexes according to their binding affinities. Since their introduction 5 years ago, the knowledge base of protein-ligand complexes has grown to the point were rederiving potentials of mean force becomes meaningful for statistical reasons. Revisiting potential of mean force (PMF) scoring (J. Med. Chem. 1999, 42, 791), we present an updated PMF04 scoring function that is based on 7152 protein-ligand complexes from the PDB. This constitutes an increase of about 10-fold compared to the knowledge base of the original PMF99 score (697 complexes). Because of the increased statistical basis of the PMF04 score, potentials for metal ions have been derived for the first time. In addition, potentials for halogens have reached statistical significance and are included also. Comparison of scoring accuracies between PMF99 and PMF04 shows an increased performance of the new score for many well-established test sets. Extending the testing of PMF scoring to the recently introduced PDBbind database containing the large number of 800 protein-ligand complexes illustrates the current limits of the approach.

Binding Sites↗

A case-based consiliarius for therapy recommendation (ICONS): computer-based advice for calculated antibiotic therapy in intensive care medicine.

We report here on the system ICONS which utilizes case-based reasoning for medical decision support. As an application domain we have chosen the medical field of 'calculated antibiotic therapy' in an intensive care medicine setting. The system ICONS which runs on a personal computer suggests adequate antibiotic therapy regimen satisfying medical and economic conditions. To speed up the process of finding an adequate antibiotic therapy for a current patient, case-based reasoning is used for finding previously documented similar cases and for modifying them according to the requirements of the current patient. To reduce the memory capacity for the documentation of cases, collections of similar cases are clustered to prototypes. Medical knowledge is represented within a hierarchy of such prototypes and cases and an additional context-sensitive background knowledge-base. A knowledge acquisition tool was programmed that allows revisions of the background medical knowledge-base by simple and comprehensive methods. In addition to the advantage of producing site-specific and time-dependent knowledge, case-based reasoning is a practical method for speeding-up the process of generating and evaluating hypotheses in medical classification tasks.

Anti-Bacterial Agents↗

Computer-assisted diagnosis of pediatric rheumatic diseases.

OBJECTIVE: AI/RHEUM is a multimedia expert system developed originally to assist in the diagnosis of rheumatic diseases in adults. In the present study we evaluated the usefulness of a modified version of this diagnostic decision support system in diagnosing childhood rheumatic diseases. METHODOLOGY: AI/RHEUM was modified by the addition of 5 new diseases to the knowledge base of the system. Criteria tables for each of the diseases included in the knowledge base were modified to suit the needs of children. The modified system was tested on 94 consecutive children seen in a pediatric rheumatology clinic. RESULTS: AI/RHEUM made the correct diagnosis in 92% of the cases when the diagnosis was available in the knowledge base of the system. It was also shown to be effective in the education of pediatric trainees through its multimedia features. CONCLUSIONS: AI/RHEUM is an expert system that may be helpful to the nonspecialist as a diagnostic decision support system and as an educational tool.

Child↗

The normal menopause transition: an overview.

OBJECTIVES: This review emphasizes results from population based studies in order to provide a reliable knowledge base and define major knowledge gaps for future research. METHODS: The review systematically addresses the following aspects of menopause research: definition of menopause and menopause transitions; age at natural menopause and at inception of perimenopause; factors affecting timing and length of menopause transitions; concurrent hormone, menstrual and vascular changes. Under each substantive heading, available reports of original research are reviewed, assessed for reliability and summarized with respect to their contribution to the knowledge base. RESULTS: From this review, the following reliable information can be summarized: median age at natural menopause in Caucasian women occurs between 50 and 51-55 years of age, is not affected by timing of surgical menopause and is affected by cigarette smoking, with current smokers having an earlier menopause by 1.5-2.0 years. No other factors appear to have an independent effect on age at menopause. There is no evidence of any secular trend in age at menopause. Evidence is accumulating that most physiological change associated with menopause either occurs or begins before the final menstrual period. CONCLUSIONS: We lack reliable information on the following: estimates of age at menopause from non-industrialized countries and diverse ethnic groups; information on perimenopause; differing perceptions of menopause, cross culturally; multi-disciplinary prospective data that can link various processes into coherent patterns.

Adult↗

Chemogenomics: drug discovery's panacea?

Chemogenomics aims towards the systematic identification of small molecules that interact with the products of the genome and modulate their biological function. This Opinion article summarizes the different knowledge-based chemogenomics strategies that are followed and outlines the challenges and opportunities that will impact drug discovery. Chemogenomics aims towards the systematic identification of small molecules that interact with the products of the genome and modulate their biological function. While historically the approach is based on efforts that systematically explore target gene families like kinases, today additional knowledge-based systematization principles are followed within early drug discovery projects which aim to biologically validate the targets and to identify starting points for chemical lead optimization. While the expectations of chemogenomics are very high, the reality of drug discovery is quite sobering with very high project attrition rates. This article summarizes the different knowledge-based chemogenomics strategies that are followed and outlines the challenges and potential opportunities that will impact drug discovery.

Chemistry, Pharmaceutical↗

A case study in ontology library construction.

The goal of our work is to facilitate the development of medical knowledge-based systems by providing a library of reusable ontologies. The availability of such a library reduces the amount of knowledge acquisition required to create knowledge bases of new applications, and makes it easier to connect a knowledge-based system to existing data bases. This article presents a case study in constructing such a library. The emphasis is on studying the principles that underly the internal structure of the library as well as on the process of constructing and using the library. We envision that, in the future, application ontologies can be constructed by the selection and refinement of generic ontologies and domain ontologies from such a library.

Artificial Intelligence↗

[The physician and the computer. 28. Expert systems].

The authors review problems of expert systems and their application in medicine. They discuss their application, structure and function. Special attention is devoted to questions which concern the users, in particular the selection of the decision making method with regard to the character of the problem and then the structure of the knowledge base. The structure of the medical knowledge base has some specific features which are formulated as the classification of rules of medical knowledge.

Expert Systems↗

Age dependence of human gastrocnemius Mg2+: fitting 31P-NMR spectra using quantum mechanics-based prior knowledge.

The age dependence of human gastrocnemius Mg2+ concentration is demonstrated. To quantitate Mg2+ concentration, an original and accurate fitting algorithm using quantum mechanics-based prior knowledge is detailed. In a group of 28 volunteers (14 females) in the age range 5-80 years, pH, PCr/ATP and Pi/ATP values in the gastrocnemius were 7.02 +/- 0.02 pH, 4.16 +/- 0.33 and 0.13 +/- 0.02, respectively and independent of age and sex. By contrast, intracellular Mg2+ concentration (mM) decreased linearly (p < 0.05) with age (Mg2+ = 0.7803 +/- 0.0247-0.0027 +/- 0.0005 * age). No difference was found between sexes. From these results, it follows that care must be taken when comparing muscle Mg2+ data from subjects of different age. The hypothesis can be put forward that during aging insufficient intake and/or increased depletion of Mg2+ (e.g., intestinal hypoabsorption or urinary leakage) may affect the musculoskeletal system.

Adenosine Triphosphate↗

The application of expert systems in the clinical laboratory.

An "expert system" consists of a knowledge base containing information of a general nature and an inference system that receives data from the user and applies the knowledge base to produce advice and explanations. An expert system stripped of its knowledge base (a tool) may be used to build new expert systems. Existing systems relevant for laboratory medicine are reviewed. The role in the laboratory of expert systems and their integration and evaluation are discussed.

Clinical Laboratory Techniques↗

META. 3. A genetic algorithm for metabolic transform priorities optimization.

META is a knowledge-based expert system that simulates the biotransformation of xenobiotics. It operates with the help of a dictionary (knowledge base) to seek target fragments in a compound and transform them to products. Here, a genetic algorithm is introduced to help build the knowledge base and optimize the performance of the methodology.

Algorithms↗

Expert systems in orthodontic clinical diagnosis.

Computer-aided diagnosis of orthodontic anomalies is of particular interest in the screening of potential patients. General practitioners and public dental health workers would find considerable benefit in a reliable automated system capable of reflecting the clinical assessments of orthodontists. This study investigates the application of an expert system (Xpert Rule) to the assessment of treatment need using thirteen traits of malocclusion to construct a knowledge base. Decision trees were generated using a pruning option available with the expert system. The results of the study are encouraging and suggest that an expert system is able to generate a useful clinical screening device. Further modification of the knowledge base is required to provide a broader range of case types, particularly those of less common occurrence. It is also suggested that the random aggregation of cases is not the most effective means of developing a knowledge base.

Algorithms↗

A virtual medical record for guideline-based decision support.

A major obstacle in deploying computer-based clinical guidelines at the point of care is the variability of electronic medical records and the consequent need to adapt guideline modeling languages, guideline knowledge bases, and execution engines to idiosyncratic data models in the deployment environment. This paper reports an approach, developed jointly by researchers at Newcastle and Stanford, where guideline models are encoded assuming a uniform virtual electronic medical record and guideline-specific concept ontologies. For implementing a guideline-based decision-support system in multiple deployment environments, we created mapping knowledge bases to link terms in the concept ontology with the terminology used in the deployment systems. Mediation components use these mapping knowledge bases to map data in locally deployed medical record architectures to the virtual medical record. We discuss the possibility of using the HL7 Reference Information Model (RIM) as the basis for a standardized virtual medical record, showing how this approach also complies with the European pre-standard ENV13606 for electronic healthcare record communication.

Decision Making, Computer-Assisted↗

Corpus-based associations provide additional morphological variants to medical terminologies.

Knowledge of morphologically derived words, as provided for medical English by the UMLS Specialist Lexicon, is useful to detect term variants for automated coding and indexing. For most other languages though, no comparable morphological knowledge base is available. We therefore endeavored to design general methods to help collect such knowledge for a given language. We propose here a method for discovering derived words in text corpora and apply it to a French medical corpus. To evaluate this method, we study its ability to suggest derived adjectives for 2,297 nouns found in the SNOMED nomenclature, which itself specifies adjectival equivalents for some of its terms. 74% of the proposed adjectives are judged correct (precision) and cover 16% of these nouns (recall), a larger amount than what SNOMED already specifies. Furthermore, the corpus suggests additional adjectives which can increase SNOMED's by 76%. We conclude that such a method can help speed up the construction of a morphological knowledge base which can increase the number of term variants in an existing controlled vocabulary.

Abstracting and Indexing↗

Total simulation of operator team behavior in emergencies at nuclear power plants.

In a large and complex system (i.e., a space aeronautics and nuclear power plant) it would be valuable to conduct operator training and support to demonstrate standard operators' behavior in coping with an anomaly caused by multiple malfunctions in which procedures would not have been stipulated previously. A system simulating operator team behavior including individual operator's cognitive behavior, his operations and physical behavior, and even verbal communication among team members, has been developed for a typical commercial nuclear power plant. This simulation model is not a scenario-based system but a complete knowledge-based system, based on the mental model that was envisaged by detailed analyses of experimental results obtained in the full-scope plant simulator. This mental model is composed of a set of knowledge bases and rules able to generate both diagnosis and prognosis depending on the observed situation even for multiple malfunctions. Simulation results of operator team behavior and plant dynamics were compared with corresponding experiments in several anomalies of multiple malfunctions. The comparison showed a reasonable agreement, so the simulation conditions were varied on cognitive task processing speed of individual operators, on team role sharing scheme, and on human machine interface (1st generation to 2nd generation control panel) to assess the sensitivity of this simulation model. Finally, it was shown that this simulation model has applications for the use of training standards and computer aided operator support systems.

Computer Simulation↗

Issues in the classification of disease instances with ontologies.

Ontologies define classes of entities and their interrelations. They are used to organize data according to a theory of the domain. Towards that end, ontologies provide class definitions (i.e., the necessary and sufficient conditions for defining class membership). In medical ontologies, it is often difficult to establish such definitions for diseases. We use three examples (anemia, leukemia and schizophrenia) to illustrate the limitations of ontologies as classification resources. We show that eligibility criteria are often more useful than the Aristotelian definitions traditionally used in ontologies. Examples of eligibility criteria for diseases include complex predicates such as ' x is an instance of the class C when at least n criteria among m are verified' and 'symptoms must last at least one month if not treated, but less than one month, if effectively treated'. References to normality and abnormality are often found in disease definitions, but the operational definition of these references (i.e., the statistical and contextual information necessary to define them) is rarely provided. We conclude that knowledge bases that include probabilistic and statistical knowledge as well as rule-based criteria are more useful than Aristotelian definitions for representing the predicates defined by necessary and sufficient conditions. Rich knowledge bases are needed to clarify the relations between individuals and classes in various studies and applications. However, as ontologies represent relations among classes, they can play a supporting role in disease classification services built primarily on knowledge bases.

Humans↗

Computer-aided decision support in hepatology.

The aim of this study was to describe and to evaluate the publications of the last 30 years devoted to computer-aided decision support in clinical hepatology. The search used Medlars and references of articles. Computer-aided decision support (CADS) was classified in two categories: statistical systems and knowledge-based systems. Two specific questionnaires were used for methodologic evaluation, one for statistical systems and one for knowledge-based systems. They were filled out independently by two observers. A total of 31 papers were selected among 55 identified between 1960 and 1991. The maximum possible for the two scores was 24. The methodologic quality ranged from 4 to 22 (median, 12) for statistical systems and from 8 to 12 (median, 9) for knowledge-based systems. The poor level of methodology could explain in part the lack of utilization of computer-aided decision support in the daily clinical practice of hepatologists.

Bayes Theorem↗

ColiGene: object-centered representation for the study of E coli gene expressivity by sequence analysis.

ColiGene is an object-centered knowledge base for the study of gene expressivity in Escherichia coli by DNA sequence analysis. This system was developed with the knowledge base management system SHIRKA. Objects represented in ColiGene are biological structures such as genes or regulatory signals. They are organized in a hierarchical structure of classes, subclasses and instances. Navigation through the knowledge base and the building of queries are made using a graphical interface. The base is coupled with the data base ACNUC which structures a specialized collection of sequences: EcoSeq. Several tools are also associated to ColiGene, either for sequence analysis or for a more general purpose. Some biological results have been obtained using ColiGene which are summarized here.

Artificial Intelligence↗

Decision support system to assist mechanical ventilation in the adult respiratory distress syndrome.

This paper presents a knowledge-based decision support system to assist mechanical ventilation in patients with the Adult Respiratory Distress Syndrome (DSSARDS). The knowledge base uses clinical algorithms developed from interviews and seminars with experts. The system contains 140 rules, applies backward chaining and was built on an IBM-PC compatible microcomputer. Clinical and physiological data and ventilator settings were used for suggestions of ventilatory support mode (VSMODE) and settings (MVSET) and for hemodynamic evaluation and therapy (HEMO). Success rates (s) and kappa coefficient (k) were used to measure agreement between DSSARDS and physicians at 4 decision steps related to: beginning of mechanical ventilation (FIRSTSET), VSMODE, MVSET and HEMO, DSSARDS prototype was evaluated in a development phase with 6 patients aged 48.6 +/- 15.9 years. Agreement results for 142 decision steps were: FIRSTSET k = 0.90, s = 0.93; VSMODE k = 0.76, s = 0.92; HEMO k = 0.58, s = 0.70, MVSET k = 0.86, s = 0.92 (p < 0.05 for all k). Improvements in the knowledge base were performed mainly in HEMO and VSMODE modules. The subsequent test phase studied 5 patients aged 54.8 +/- 11.0 years in a total of 900 decision steps. Results were: FIRSTSET k = 0.93, s = 0.95; VSMODE k = 0.93, s = 0.96; HEMO k = 0.97, s = 0.99, MVSET k = 0.96, s = 0.97 (p < 0.05 for all k). The results indicate significant agreement between DSSARDS and physicians for all decision steps. This suggests that DSSARDS may be used as a support for decision making and a training tool for mechanical ventilation in patients with the adult respiratory distress syndrome.

Adult↗