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PubMed1992

The way to adequate control of microbial processes passes via real-time knowledge-based supervision.

Knowledge-based supervision is viewed as a major tool for achieving high-performance control of microbial processes. By providing an adequate insight into the integral state of the cell culture, knowledge-based supervisory systems allow for monitoring and handling various important phenomena which usually remain outside the scope of the conventional control approach. The present paper focuses on the development of a computer system for knowledge-based supervision of bioprocesses. Its application to the control of fed-batch cultivation of recombinant Escherichia coli for phenylalanine production is also discussed.

Biotechnology↗
PubMed2002

PharmGKB: the Pharmacogenetics Knowledge Base.

The Pharmacogenetics Knowledge Base (PharmGKB; http://www.pharmgkb.org/) contains genomic, phenotype and clinical information collected from ongoing pharmacogenetic studies. Tools to browse, query, download, submit, edit and process the information are available to registered research network members. A subset of the tools is publicly available. PharmGKB currently contains over 150 genes under study, 14 Coriell populations and a large ontology of pharmacogenetics concepts. The pharmacogenetic concepts and the experimental data are interconnected by a set of relations to form a knowledge base of information for pharmacogenetic researchers. The information in PharmGKB, and its associated tools for processing that information, are tailored for leading-edge pharmacogenetics research. The PharmGKB project was initiated in April 2000 and the first version of the knowledge base went online in February 2001.

Biotransformation↗
PubMedDate not supplied

Evaluating and validating very large knowledge-based systems.

Most knowledge-based systems for use in medicine have been developed in response to specific problems such as the diagnosis of abdominal or chest pain in an accident and emergency department, or the diagnosis and treatment of meningitis. There is a role for a general decision support system capable of answering queries about any aspect of medicine, particularly in primary care. However, evaluating such a knowledge base requires more elaborate methodology than a simple iterative test and refine cycle. At the design stage an adequate knowledge base structure is required to allow focused modification of the knowledge base when errors are discovered. During the prolonged evaluation cycle the partially formed knowledge base must be tested with such techniques as validation checks for consistency and completeness and examination of characteristics of problem-solving procedures. Finally a variety of criteria that represent the performance, robustness, flexibility, predictability, validity, coverage, relevance and congruity of the knowledge base are needed for a full description of the system's worth. Two case studies from the Oxford System of Medicine project are provided as examples of this philosophy: validating specific medical facts and comparing two methods for aggregating reasoning for and against a decision option.

Artificial Intelligence↗
PubMed2000

Case-based reasoning for medical knowledge-based systems.

In many domains Case-based Reasoning (CBR) has become a successful technique for knowledge-based systems. In medical domains, attempts to apply the complete CBR cycle are rather exceptional. Some systems have recently been developed, which on the one hand use only parts of the CBR method, mainly the retrieval, and on the other hand enrich the method by a generalisation step to fill the knowledge gap between the specificity of single cases and general rules. So, in this paper we discuss the appropriateness of CBR for medical knowledge-based systems, point out problems, limitations and possibilities how they can partly be overcome.

Artificial Intelligence↗
PubMed1997

Neutral networks in protein space: a computational study based on knowledge-based potentials of mean force.

BACKGROUND: Many protein sequences, often unrelated, adopt similar folds. Sequences folding into the same shape thus form subsets of sequence space. The shape and the connectivity of these sets have implications for protein evolution and de novo design. RESULTS: We investigate the topology of these sets for some proteins with known three-dimensional structure using inverse folding techniques. First, we find that sequences adopting a given fold do not cluster in sequence space and that there is no detectable sequence homology among them. Nevertheless, these sequences are connected in the sense that there exists a path such that every sequence can be reached from every other sequence while the fold remains unchanged. We find similar results for restricted amino acid alphabets in some cases (e. g. ADLG). In other cases, it seems impossible to find sequences with native-like behavior (e.g. QLR). These findings seem to be independent of the particular structure considered. CONCLUSIONS: Amino acid sequences folding into a common shape are distributed homogeneously in sequence space. Hence, the connectivity of the set of these sequences implies the existence of very long neutral paths on all examined protein structures. Regarding protein design, these results imply that sequences with more or less arbitrary chemical properties can be attached to a given structural framework. But we also observe that designability varies significantly among native structures. These features of protein sequence space are similar to what has been found for nucleic acids.

Amino Acids↗
PubMedDate not supplied

A UMLS-based knowledge acquisition tool for rule-based clinical decision support system development.

Decision support systems in the medical field have to be easily modified by medical experts themselves. The authors have designed a knowledge acquisition tool to facilitate the creation and maintenance of a knowledge base by the domain expert and its sharing and reuse by other institutions. The Unified Medical Language System (UMLS) contains the domain entities and constitutes the relations repository from which the expert builds, through a specific browser, the explicit domain ontology. The expert is then guided in creating the knowledge base according to the pre-established domain ontology and condition-action rule templates that are well adapted to several clinical decision-making processes. Corresponding medical logic modules are eventually generated. The application of this knowledge acquisition tool to the construction of a decision support system in blood transfusion demonstrates the value of such a pragmatic methodology for the design of rule-based clinical systems that rely on the highly progressive knowledge embedded in hospital information systems.

Artificial Intelligence↗
PubMed1993

Consistency enforcement in medical knowledge base construction.

Some aspects of knowledge base creation can be partially or completely automated, resulting in higher quality and smaller effort. Computer assistance is particularly valuable in ensuring the internal consistency of a knowledge base. The article describes several techniques for consistency enforcement in QMR-KAT, an interactive knowledge base editor for the INTERNIST-I/QMR medical knowledge base. Two strategies that improve consistency are applicable to a wide range of situations. The first strategy prevents simple (but common) inconsistencies. The second strategy reveals facts that are potentially (but not necessarily) inconsistent with known data, and may require further evaluation. Both strategies use the contents of the existing knowledge base in the evaluation of new facts.

Artificial Intelligence↗
PubMed1999

Workflow analysis and evidence-based medicine: towards integration of knowledge-based functions in hospital information systems.

The large extent and complexity of scientific evidence described in the concept of evidence-based medicine often overwhelms clinicians who want to apply best external evidence. Hospital Information Systems usually do not provide knowledge-based functions to support context-sensitive linking to external information sources. Knowledge-based components need specific data, which must be entered manually and should be well adapted to clinical environment to be accepted by clinicians. This paper describes a workflow-based approach to understand and visualize clinical reality as a preliminary to designing software applications, and possible starting points for further software development.

Artificial Intelligence↗
PubMed1990

Knowledge bases in medicine: a review.

Efforts to represent knowledge effectively have been central to progress in various aspects of medical informatics. These efforts range from relatively simple "electronic textbooks" to fairly sophisticated knowledge-based systems, which function as well as, or even better than, human experts faced with similar problems. Knowledge bases have been developed in many fields, but the relatively limited domains and structured language of medicine, as well as the importance of information in the provision of good medical care, have made research in medical knowledge representation an area of intense activity. This paper reviews representative knowledge bases and knowledge-based systems in medicine: electronic textbooks such as PDQ and the Hepatitis Knowledge Base (HKB), rule-based systems such as MYCIN, causal models (e.g., CASNET), and hypothesis- or frame-based systems, exemplified by PIP and INTERNIST-1. The paper describes the relationships among divergent approaches and provides a sense of current and future trends. It examines problems in knowledge-based systems, particularly in knowledge representation and acquisition, and the responses to these challenges. The latter include the use of domain-independent software shells for constructing knowledge bases, the adaptation and use of previously existing knowledge bases, and multiple uses of the same knowledge base for different purposes.

Artificial Intelligence↗
PubMed2000

The use of knowledge-based systems to improve medical knowledge about urine analysis.

Urine protein diagnostics has developed into a routine method for screening and monitoring kidney diseases. It is based on the quantitative measurement of total protein, albumin, alpha(1)-microglobulin, immunoglobulin G and alpha(2)-macroglobulin (all related to urine creatinine), as well as a dipstick screening. The excretion pattern of the marker proteins allows differentiation of haematuria, leukocyturia and proteinuria and to assign them to prerenal, renal and postrenal causes. In order to provide the clinical partner not only with pure analytical results, but to support clinical decision making by an interpretative report, a urine protein expert system (UPES) has been developed. Based on a database containing more than 500 excretion patterns of patients with known diagnoses, a knowledge base was extracted. In its modules plausibility control, glomerular filtration rate, hematuria, leukocyturia and proteinuria, IF-THEN-rules interpret the given patterns and select matching text elements. The knowledge base has been integrated in the modern expert system shell WILAS, and the resulting interpretation system has been thoroughly verified and validated. An internal acceptance study revealed that urine protein differentiation is widely accepted as a diagnostic option and that its interpretation, provided with the help of UPES, is appreciated as a service. In an external study, the usability of UPES in routine and its knowledge representation was evaluated in 11 centres consisting of laboratories and nephrological partners. Over seven months, more than 500 cases were interpreted using UPES and documented by questionnaires. The discussion of the results at a user conference revealed that the problem of analytical standardisation as well as the common definition of diagnostic terms by laboratory staff and clinicians play a crucial role for the use of knowledge-based systems in laboratory medicine. Whereas the user interface of UPES was judged very heterogeneously, the correctness of the proposed interpretations was unanimously rated as "good". As a result of the evaluation, the user interface has been modernised. The knowledge base has been extended to address paediatric issues as well, and to take clinical information and previous findings into consideration.

Evaluation Studies as Topic↗
PubMed2004

HYPROSP: a hybrid protein secondary structure prediction algorithm--a knowledge-based approach.

We develop a knowledge-based approach (called PROSP) for protein secondary structure prediction. The knowledge base contains small peptide fragments together with their secondary structural information. A quantitative measure M, called match rate, is defined to measure the amount of structural information that a target protein can extract from the knowledge base. Our experimental results show that proteins with a higher match rate will likely be predicted more accurately based on PROSP. That is, there is roughly a monotone correlation between the prediction accuracy and the amount of structure matching with the knowledge base. To fully utilize the strength of our knowledge base, a hybrid prediction method is proposed as follows: if the match rate of a target protein is at least 80%, we use the extracted information to make the prediction; otherwise, we adopt a popular machine-learning approach. This comprises our hybrid protein structure prediction (HYPROSP) approach. We use the DSSP and EVA data as our datasets and PSIPRED as our underlying machine-learning algorithm. For target proteins with match rate at least 80%, the average Q3 of PROSP is 3.96 and 7.2 better than that of PSIPRED on DSSP and EVA data, respectively.

Algorithms↗
PubMed1995

Guideline based care: the challenge for knowledge based decision support.

The EPISTOL action was included in the accompanying measures of AIM '91-'94 as a strategic study, aimed at clarifying the impact in the near future of knowledge based systems and techniques for the health sector, and provide recommendations with respect to the research and development work required within this period. In all the EPISTOL events, namely the Munich and Brussels workshops, the topic of clinical guidelines and protocol and based care raised considerable interest. This paper summarises these discussions, focussing on the KBS support for clinical guidelines.

Artificial Intelligence↗
PubMed2005

Structural semantic interconnections: a knowledge-based approach to word sense disambiguation.

Word Sense Disambiguation (WSD) is traditionally considered an Al-hard problem. A break-through in this field would have a significant impact on many relevant Web-based applications, such as Web information retrieval, improved access to Web services, information extraction, etc. Early approaches to WSD, based on knowledge representation techniques, have been replaced in the past few years by more robust machine learning and statistical techniques. The results of recent comparative evaluations of WSD systems, however, show that these methods have inherent limitations. On the other hand, the increasing availability of large-scale, rich lexical knowledge resources seems to provide new challenges to knowledge-based approaches. In this paper, we present a method, called structural semantic interconnections (SSI), which creates structural specifications of the possible senses for each word in a context and selects the best hypothesis according to a grammar G, describing relations between sense specifications. Sense specifications are created from several available lexical resources that we integrated in part manually, in part with the help of automatic procedures. The SSI algorithm has been applied to different semantic disambiguation problems, like automatic ontology population, disambiguation of sentences in generic texts, disambiguation of words in glossary definitions. Evaluation experiments have been performed on specific knowledge domains (e.g., tourism, computer networks, enterprise interoperability), as well as on standard disambiguation test sets.

Algorithms↗
PubMed1987

Knowledge-based indexing of the medical literature: the Indexing Aid Project.

This article describes the Indexing Aid Project for conducting research in the areas of knowledge representation and indexing for information retrieval in order to develop interactive knowledge-based systems for computer-assisted indexing of the periodical medical literature. The system uses an experimental frame-based knowledge representation language, FrameKit, implemented in Franz Lisp. The initial prototype is designed to interact with trained MEDLINE indexers who will be prompted to enter subject terms as slot values in filling in document-specific frame data structures that are derived from the knowledge-base frames. In addition, the automatic application of rules associated with the knowledge-base frames produces a set of Medical Subject Heading (MeSH) keyword indices to the document. Important features of the system are representation of explicit relationships through slots which express the relations; slot values, restrictions, and rules made available by inheritance through "is-a" hierarchies; slot values denoted by functions that retrieve values from other slots; and restrictions on slot values displayable during data entry.

Abstracting and Indexing↗
PubMed1995

Knowledge-acquisition tools for medical knowledge-based systems.

Knowledge-based systems (KBS) have been proposed to solve a large variety of medical problems. A strategic issue for KBS development and maintenance are the efforts required for both knowledge engineers and domain experts. The proposed solution is building efficient knowledge acquisition (KA) tools. This paper presents a set of KA tools we are developing within a European Project called GAMES II. They have been designed after the formulation of an epistemological model of medical reasoning. The main goal is that of developing a computational framework which allows knowledge engineers and domain experts to interact cooperatively in developing a medical KBS. To this aim, a set of reusable software components is highly recommended. Their design was facilitated by the development of a methodology for KBS construction. It views this process as comprising two activities: the tailoring of the epistemological model to the specific medical task to be executed and the subsequent translation of this model into a computational architecture so that the connections between computational structures and their knowledge level counterparts are maintained. The KA tools we developed are illustrated taking examples from the behavior of a KBS we are building for the management of children with acute myeloid leukemia.

Algorithms↗
PubMed2001

Exploring protein sequence space using knowledge-based potentials.

Knowledge-based potentials can be used to decide whether an amino acid sequence is likely to fold into a prescribed native protein structure. We use this idea to survey the sequence-structure relations in protein space. In particular, we test the following two propositions which were found to be important for efficient evolution: the sequences folding into a particular native fold form extensive neutral networks that percolate through sequence space. The neutral networks of any two native folds approach each other to within a few point mutations. Computer simulations using two very different potential functions, M. Sippl's PROSA pair potential and a neural network based potential, are used to verify these claims.

Amino Acid Sequence↗
PubMedDate not supplied

Metareasoning and meta-level learning in a hybrid knowledge-based architecture.

Ahybrid knowledge-based architecture integrates different problem solvers for the same (sub)task through a control unit operating at a meta-level, the metareasoner, which coordinates the use of, and the communication between, the different problem solvers. A problem solver is defined to be an association between a knowledge intensive (sub)task, an inference mechanism and a knowledge domain view operated by the inference mechanism in order to perform the (sub)task. Important issues in a hybrid system are the metareasoning and learning aspects. Metareasoning encompasses the functions performed by the metareasoner, while learning reflects the ability of the system to evolve on the basis of its experiences in problem solving. Learning occurs at different levels, learning at the meta-level and learning at the level of the specific problem solvers. Meta-level learning reflects the ability of the metareasoner to improve the overall performance of the hybrid system by improving the efficiency of meta-level tasks. Meta-level tasks include the initial planning of problem solving strategies and the dynamic adaptation of chosen strategies depending on new events occurring dynamically during problem solving. In this paper we concentrate on metareasoning and meta-level learning in the context of a hybrid architecture. The theoretical arguments presented in the paper are demonstrated in practice through a hybrid knowledge-based prototype system for the domain of breast cancer histopathology.

Algorithms↗