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PubMed1995

A method for creating an enormous medical knowledge base.

Using a high-density knowledge representation method designed by us, we have developed the Enormous Knowledge Base of Disease Diagnosis Criteria (EKBDDC). It contains diagnostic criteria of 1001 diagnostic entities and describes nearly 4000 items of diagnostic indicators. It is the core of a huge medical project--Electronic Brain Medical Erudite (EBME). This enormous knowledge base was implemented initially on a low-cost popular microcomputer, which can aid in prompting of typical disease and in teaching of diagnosis. This knowledge base will be constantly expanded and adapted to the need of diagnosing of atypical diseases. By means of a software interface it will be connected with the international medical information systems. We have also explored an assembling technique of medical knowledge base. To test the behavior of EBME we performed a series of trials with a total of 815 cases. The diagnostic accordance rates were 89.7, 89.4, and 85%, respectively. It demonstrated that this system should be improved before clinical application.

Algorithms↗

PubMed1988

Knowledge-based educational systems.

In knowledge-based educational systems, the key concept is that information and procedures are represented in the same data structure. These structures can search for each other in flexible and, consequently, very robust ways. At the Air Force Human Resources Laboratory (AFHRL), our researchers are building computer environments that know what they know, know how people can best use them, and know how to draw inferences about their state--self-referential electronic tutors. In September 1986, artificial intelligence researchers participated in AFHRL's Research Planning Forum for Intelligent Tutorial Systems (ITS). This essay reviews the state of the philosophy, art, and science of artificial intelligence (AI) approaches to education. Then it summarizes the research issues which were presented, discussed, and better defined in this Forum--namely the nature and representation of 1) expertise modules, 2) student diagnostic modules, 3) adaptive instructional and curriculum modules, 4) instructional environments, and 5) man-machine interfaces. Advances in artificial intelligence, cognitive science, and instructional discourse have provided a means for investigating human learning, for representing an individual's own "knowledge processing." Research and development in knowledge-based educational systems seems promising, not only for helping people learn how to perform complex tasks, but also for explicitly expressing how people learn to learn. Therefore, would it not be wise to establish a scientific legacy for the development of effective knowledge-based tutorial systems which is informed by the best studies of mind and meaning, language and thought, purpose and paradox?

Artificial Intelligence↗

PubMed1991

Knowledge-based support for a physician's workstation.

We describe knowledge-based support for a Physician's Workstation prototype. Our knowledge base uses a qualitative simulation model of patient physiology. We present the motivation behind our design, discuss the components of the knowledge base, and show how the knowledge base supports a physician's workstation in the patient management process. We describe a graphical knowledge base editor used by the domain expert for knowledge acquisition, and a graphical knowledge base presenter which monitors the qualitative simulation during patient event processing.

Artificial Intelligence↗

PubMed1992

A knowledge base for predicting protein localization sites in eukaryotic cells.

To automate examination of massive amounts of sequence data for biological function, it is important to computerize interpretation based on empirical knowledge of sequence-function relationships. For this purpose, we have been constructing a knowledge base by organizing various experimental and computational observations as a collection of if-then rules. Here we report an expert system, which utilizes this knowledge base, for predicting localization sites of proteins only from the information on the amino acid sequence and the source origin. We collected data for 401 eukaryotic proteins with known localization sites (subcellular and extracellular) and divided them into training data and testing data. Fourteen localization sites were distinguished for animal cells and 17 for plant cells. When sorting signals were not well characterized experimentally, various sequence features were computationally derived from the training data. It was found that 66% of the training data and 59% of the testing data were correctly predicted by our expert system. This artificial intelligence approach is powerful and flexible enough to be used in genome analyses.

Algorithms↗

PubMed1980

The hepatitis knowledge base. A prototype information transfer system.

The Hepatitis Knowledge Base is a prototype computerized information-transfer system aimed at supporting the health practitioner's day-to-day diagnosis, prognosis, and treatment decisions concerned with viral hepatitis. An overview of information-transfer problems in biomedicine is presented and issues pertinent to the knowledge-base concept are discussed. The following research activities are described: selection and organization of the content of the initial draft of the complete Hepatitis Knowledge Base; the method for consensus development by a nationally distributed panel of collaborating experts on the subject matter; methods for updating the knowledge base and maintaining its currency over time; use of a computer conferencing network as the principal medium of communication among the geographically dispersed experts and the project staff; support of on-line access to the knowledge-base contents; and formative evaluation of the above methods and limited field testing of the access system.

Hepatitis, Viral, Human↗

PubMed2006

Knowledge-based potentials in protein design.

Knowledge-based potentials are statistical parameters derived from databases of known protein properties that empirically capture aspects of the physical chemistry of protein structure and function. These potentials play a key role in protein design by improving the accuracy of physics-based models of interatomic interactions and enhancing the computational efficiency of the design process by limiting the complexity of searching sequence space. Recently, knowledge-based potentials (in isolation or in combination with physics-based potentials) have been applied to the modification of existing protein function, the redesign of natural protein folds and the complete design of a non-natural protein fold. In addition, knowledge-based potentials appear to be providing important information about the global topology of amino acid interactions in natural proteins. A detailed study of the methods and products of these protein design efforts promises to greatly expand our understanding of proteins and the evolutionary process that created them.

Databases, Protein↗

PubMed1992

A specialized framework for medical diagnostic knowledge-based systems.

For a knowledge-based system (KBS) to exhibit an intelligent behavior, it must be endowed with knowledge enabling it to represent the expert's strategies. The elicitation task is inherently difficult for strategic knowledge, because strategy is often tacit, and, even when it has been made explicit, it is not an easy task to describe it in a form which may be directly translated and implemented into a program. This paper describes a Specialized Framework for Medical Diagnostic Knowledge-Based Systems that can help an expert in the process of building KBSs in a medical domain. The framework is based on an epistemological model of diagnostic reasoning which has proven to be helpful in describing the diagnostic process in terms of the tasks that it is composed of. It allows a straightforward modeling of diagnostic reasoning at the knowledge level by the domain expert, thus helping to convey domain-dependent strategies into the target KBS.

Artificial Intelligence↗

PubMed1991

A specialized framework for Medical Diagnostic Knowledge Based Systems.

To have a knowledge based system (KBS) exhibiting an intelligent behavior, it must be endowed even with knowledge able to represent the expert's strategies, other than with domain knowledge. The elicitation task is inherently difficult for strategic knowledge, because strategy is often tacit, and, even when it has been made explicit, it is not an easy task to describe it in a form that may be directly translated and implemented into a program. This paper describes a Specialized Framework for Medical Diagnostic Knowledge Based Systems able to help an expert in the process of building KBSs in a medical domain. The framework is based on an epistemological model of diagnostic reasoning which has proved to be helpful in describing the diagnostic process in terms of the tasks by which it is composed of.

Artificial Intelligence↗

PubMed1999

A process to maintain the quality of a computerized knowledge base.

As part of a project to develop knowledge-based reminders for the outpatient setting, we developed a process to help maintain the quality of the knowledge base. The knowledge engineering process involved many parties, including several domain experts, a knowledge engineer, and a programmer and a process was necessary to assure that information transfer among individuals did not become confused. An MS Access database was created to store, among other data, textual versions of the rules as they evolved over time. In a 9-month period 36 rules were entered into the database. Of those, 17 are still active in their original form. The remaining 19 underwent various types of modifications; these changes were tracked in the database. Processes and tools to maintain knowledge bases are necessary if the benefits of clinical decision support systems are to be realized and investments in knowledge engineering are to be protected.

Artificial Intelligence↗

PubMed1989

Validating the knowledge base of a therapy planning system.

Validation of expert system knowledge bases has proved to be difficult. This paper presents a description of a system called ScriptGen that generates test data for validating the knowledge base of the ONCOCIN cancer therapy planning system. Because of the size and complexity of the ONCOCIN knowledge base, we require tools for automated validation. ScriptGen, which applies techniques developed in testing both traditional software and expert systems, uses a parallel model of the ONCOCIN knowledge base and its own inference engine to generate test cases. We derived the limits of the system from a study that seeded errors into an existing knowledge base.

Antineoplastic Combined Chemotherapy Protocols↗

PubMed1995

Terminological reference of a knowledge-based system: the data dictionary.

The development of open and integrated knowledge bases makes new demands on the definition of the used terminology. The definition should be realized in a data dictionary separated from the knowledge base. Within the works done at a reference model of medical knowledge, a data dictionary has been developed and used in different applications: a term definition shell, a documentation tool and a knowledge base. The data dictionary includes that part of terminology, which is largely independent of a certain knowledge model. For that reason, the data dictionary can be used as a basis for integrating knowledge bases into information systems, for knowledge sharing and reuse and for modular development of knowledge-based systems.

Artificial Intelligence↗

PubMed2005

PharmGKB: the pharmacogenetics and pharmacogenomics knowledge base.

The Pharmacogenetics and Pharmacogenomics Knowledge Base (PharmGKB) is an interactive tool for researchers investigating how genetic variation effects drug response. The PharmGKB web site, www.pharmgkb.org, displays genotype, molecular, and clinical primary data integrated with literature, pathway representations, protocol information, and links to additional external resources. Users can search and browse the knowledge base by genes, drugs, diseases, and pathways. Registration is free to the entire research community but subject to an agreement to respect the rights and privacy of the individuals whose information is contained within the database. Registered users can access and download primary data to aid in the design of future pharmacogenetics and pharmacogenomics studies.

Databases, Factual↗

PubMedDate not supplied

Multiparameter case studies using knowledge-based systems in hematology.

Knowledge-based systems in diagnostic medicine are often used for teaching medical decision making. We have extended the educational value of our suite of hematology decision support systems (peripheral blood analysis, flow cytometry immunophenotyping and DNA analysis, and bone marrow morphology) by creating case studies in a hypertext format. The case studies use the knowledge-based system screens as a background. Digitized images of blood and bone marrow smears from the teaching cases can be accessed on-line. The case studies, which are designed primarily for technologists and physicians, emphasize the proper multiparameter approach to hematopathology diagnosis.

Decision Support Techniques↗

PubMed2005

Knowledge-based framework for hypothesis formation in biochemical networks.

MOTIVATION: The current knowledge about biochemical networks is largely incomplete. Thus biologists constantly need to revise or extend existing knowledge. The revision and/or extension are first formulated as theoretical hypotheses, then verified experimentally. Recently, biological data have been produced in great volumes and in diverse formats. It is a major challenge for biologists to process these data to reason about hypotheses. Many computer-aided systems have been developed to assist biologists in undertaking this challenge. The majority of the systems help in finding 'pattern' in data and leave the reasoning to biologists. A few systems have tried to automate the reasoning process of hypothesis formation. These systems generate hypotheses from a knowledge base and given observations. A main drawback of these knowledge-based systems is the knowledge representation formalisms they use. These formalisms are mostly monotonic and are now known to be not quite suitable for knowledge representation, especially in dealing with the inherently incomplete knowledge about biochemical networks. RESULTS: We present a knowledge-based framework for hypothesis formation for biochemical networks. The framework has been implemented by extending BioSigNet-RR-a knowledge based system that supports elaboration-tolerant representation and non-monotonic reasoning. Features of the extended system are illustrated by a case study of the p53 signal network. AVAILABILITY: http://www.biosignet.org

Algorithms↗

PubMed2000

Improvement in tangential breast planning efficiency using a knowledge-based expert system.

A knowledge-based expert system was developed for the purpose of improving radiotherapy planning efficiency for a standardized, tangential breast technique. Treatment parameters pertaining to 150 previously planned patients were used for correlating the midplane breast contour of a new patient with an appropriate set of tangential beam weights and wedge angles; other treatment parameters including, planning target volume and isocenter, were specified by a radiation oncologist. Treatment plans generated by the expert system approach and a traditional, dosimetric approach were compared and rated prospectively in 45 patients. In addition, planning time was measured for both approaches. A performance rating of 97% was achieved for the expert system, in which an artificial neural network was used to correlate breast contours to treatment parameters, and approximately 30 minutes per patient was saved in treatment planning time. This high performance rating validated various assumptions concerning the expert system: namely, that the resultant dose distribution was not influenced by tangential field width (within the range of 7 to 12 cm), nominal beam energy (6 MV), or wedge type (physical vs. enhanced dynamic). Hence, the knowledge base may be directly transferable to other cancer centers using the same breast technique, and suggests that a global resource of radiotherapy treatment plans as well as planning strategies, categorized by treatment site, stage, and technique, may be viable.

Breast Neoplasms↗

PubMed1999

Cyclophosphamide in the treatment of pulmonary diseases: survey of use, training, and practitioner knowledge base.

OBJECTIVE: To assess pulmonologists' use, training in the use, and knowledge base of the drug cyclophosphamide. DESIGN: Survey through questionnaire. Testing of knowledge base before and after instructional conference. PARTICIPANTS AND METHODS: Pulmonologists (94 attendings, 31 fellows), selected randomly at the 1996 and 1997 annual meetings of The American Thoracic Society, completed surveys of their use and training in the use of cyclophosphamide. Thirty-five attending at the 1998 meeting completed a test of knowledge base of the drug. Members of the pulmonary teaching service at The University of Chicago Hospitals completed the test before and after a case-based conference designed to educate pulmonologists in the use of the drug. RESULTS: Forty-three percent of the attending pulmonologists and 55% of the fellows were currently using the drug in the management of their patients; 77% of the attending pulmonologists had prescribed the drug in the past. Nonmalignant diseases for which the drug was prescribed included usual interstitial pneumonitis/desquamative interstitial pneumonitis, vasculitis, collagen vascular disease, constrictive bronchiolitis, sarcoid, and Goodpasture's disease. Sixty-eight percent of attending pulmonologists and 81% of fellows had no training in the drug's use. Of the attending pulmonologists who made use of the drug, 64% were prescribing and managing its use themselves. Of those who prescribed and managed the drug's use themselves, 65% had had no training in its use. Of those fellows who prescribed and managed the drug's use themselves, 73% had had no training in the drug's use. On knowledge-based testing, the average correct score was 30 +/- 10%. With an educational conference, average pre- and post-test scores rose from 40 +/- 10% to 80 +/- 10% (p < 0.001). CONCLUSION: Cyclophosphamide had been used by the vast majority of pulmonologists, either currently or in the past, for a wide variety of lung diseases. Its use is commonly managed by physicians who have no specific training relevant to this agent. Practitioner knowledge base of the drug is poor, and case-based conferences in fellowship may be an effective means of imparting information concerning this drug.

Congresses as Topic↗

PubMed2004

Native atom types for knowledge-based potentials: application to binding energy prediction.

Knowledge-based potentials have been found useful in a variety of biophysical studies of macromolecules. Recently, it has also been shown in self-consistent studies that it is possible to extract quantities consistent with pair potentials from model structural databases. In this study, we attempt to extend the results obtained from these self-consistent studies toward the extraction of realistic pair potentials from the Protein Data Bank (PDB). The new method utilizes a clustering approach to define atom types within the PDB consistent with the optimal effective pairwise potential. The method has been integrated into the SMoG drug design package, resulting in an improved approach for the rapid and accurate estimation of binding affinities from structural information. Using this approach, it is possible to generate simple knowledge-based potentials that correlate (R = 0.61) with experimental binding affinities in a database of 118 diverse complexes. Furthermore, predictions performed on a random 1/3 of the database consistently show an average unsigned error of 1.5 log Ki units. It is also possible to generate specialized knowledge-based potentials, targeted to specific protein families. This approach is capable of generating potentials that correlate strongly with experimental binding affinities within these families (R = 0.8-0.9). Predictions on 1/3 of these family databases yield average unsigned errors ranging from 1.1 to 1.3 log Ki units. In summary, we describe a physically motivated approach to optimizing knowledge-based potentials for binding energy prediction that can be integrated into a variety of stages within a lead discovery protocol.

Algorithms↗

PubMed2001

Medical knowledge reengineering--converting major portions of the UMLS into a terminological knowledge base.

We describe a semi-automatic knowledge engineering approach for converting the human anatomy and pathology portion of the UMLS metathesaurus into a terminological knowledge base. Particular attention is paid to the proper representation of part-whole hierarchies, which complement taxonomic ones as a major hierarchy-forming principle for anatomical knowledge. Our approach consists of four steps. First, concept definitions are automatically generated from the metathesaurus, with LOOM as the target language. Second, integrity checking of the emerging taxonomic and partonomic hierarchies is automatically carried out by the terminological classifier. Third, terminological cycles and inconsistencies are manually eliminated and, in the last step, the knowledge base built this way is incrementally refined by a medical expert. Our experiments were run on a terminological knowledge base which is composed of 164,000 concepts and 76,000 relations. Empirical evidence for the lack of logical consistency, adequacy and improper granularity of the UMLS knowledge source is given, and finally, assessments of what kind of efforts are needed to render the formal target representation structures complete and empirically adequate.

Anatomy↗