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PubMed2002

Of truth and pathways: chasing bits of information through myriads of articles.

Knowledge on interactions between molecules in living cells is indispensable for theoretical analysis and practical applications in modern genomics and molecular biology. Building such networks relies on the assumption that the correct molecular interactions are known or can be identified by reading a few research articles. However, this assumption does not necessarily hold, as truth is rather an emerging property based on many potentially conflicting facts. This paper explores the processes of knowledge generation and publishing in the molecular biology literature using modelling and analysis of real molecular interaction data. The data analysed in this article were automatically extracted from 50000 research articles in molecular biology using a computer system called GeneWays containing a natural language processing module. The paper indicates that truthfulness of statements is associated in the minds of scientists with the relative importance (connectedness) of substances under study, revealing a potential selection bias in the reporting of research results. Aiming at understanding the statistical properties of the life cycle of biological facts reported in research articles, we formulate a stochastic model describing generation and propagation of knowledge about molecular interactions through scientific publications. We hope that in the future such a model can be useful for automatically producing consensus views of molecular interaction data.

Algorithms↗
PubMed2001

Towards integration of clinical decision support in commercial hospital information systems using distributed, reusable software and knowledge components.

PROBLEM: Clinicians' acceptance of clinical decision support depends on its workflow-oriented, context-sensitive accessibility and availability at the point of care, integrated into the Electronic Patient Record (EPR). Commercially available Hospital Information Systems (HIS) often focus on administrative tasks and mostly do not provide additional knowledge based functionality. Their traditionally monolithic and closed software architecture encumbers integration of and interaction with external software modules. Our aim was to develop methods and interfaces to integrate knowledge sources into two different commercial hospital information systems to provide the best decision support possible within the context of available patient data. METHODS: An existing, proven standalone scoring system for acute abdominal pain was supplemented by a communication interface. In both HIS we defined data entry forms and developed individual and reusable mechanisms for data exchange with external software modules. We designed an additional knowledge support frontend which controls data exchange between HIS and the knowledge modules. Finally, we added guidelines and algorithms to the knowledge library. RESULTS: Despite some major drawbacks which resulted mainly from the HIS' closed software architectures we showed exemplary, how external knowledge support can be integrated almost seamlessly into different commercial HIS. This paper describes the prototypical design and current implementation and discusses our experiences.

Abdominal Pain↗
PubMed2004

Extracting human protein interactions from MEDLINE using a full-sentence parser.

MOTIVATION: The living cell is a complex machine that depends on the proper functioning of its numerous parts, including proteins. Understanding protein functions and how they modify and regulate each other is the next great challenge for life-sciences researchers. The collective knowledge about protein functions and pathways is scattered throughout numerous publications in scientific journals. Bringing the relevant information together becomes a bottleneck in a research and discovery process. The volume of such information grows exponentially, which renders manual curation impractical. As a viable alternative, automated literature processing tools could be employed to extract and organize biological data into a knowledge base, making it amenable to computational analysis and data mining. RESULTS: We present MedScan, a completely automated natural language processing-based information extraction system. We have used MedScan to extract 2976 interactions between human proteins from MEDLINE abstracts dated after 1988. The precision of the extracted information was found to be 91%. Comparison with the existing protein interaction databases BIND and DIP revealed that 96% of extracted information is novel. The recall rate of MedScan was found to be 21%. Additional experiments with MedScan suggest that MEDLINE is a unique source of diverse protein function information, which can be extracted in a completely automated way with a reasonably high precision. Further directions of the MedScan technology improvement are discussed. AVAILABILITY: MedScan is available for commercial licensing from Ariadne Genomics, Inc.

Abstracting and Indexing↗
PubMed1998

Speech intelligibility assessment in a helium environment. II. The speech intelligibility index.

The Speech Intelligibility Index (SII) was measured for Navy divers participating in two saturation deep dives and for a group of nondivers to test different communication systems and their components. These SIIs were validated using the Speech Perception in Noise (SPIN) test and the Griffiths version of the Modified Rhyme Test (GMRT). Our goal was to determine if either of these assessments was sensitive enough to provide an objective measure of speech intelligibility when speech was processed through different helmets and helium speech unscramblers (HSUs). Results indicated that SII values and percent intelligibility decreased incrementally as background noise level increased. SIIs were very reliable across the different groups of subjects indicating that the SII was a strong measurement for predicting speech intelligibility to compare linear system components such as helmets. The SII was not useful in measuring intelligibility through nonlinear devices such as HSUs. The speech intelligibility scores on the GMRT and SPIN tests were useful when the system component being compared had a large measurable difference, such as in helmet type. However, when the differences were more subtle, such as differences in HSUs, neither the SPIN nor the GMRT appeared sensitive enough to make such distinctions. These results have theoretical as well as practical value for measuring the quality and intelligibility of helium speech enhancement systems.

Adolescent↗
PubMed2002

Challenges in implementing a knowledge editor for the Arden Syntax: knowledge base maintenance and standardization of database linkages.

CONTEXT: Incorporation of research findings into clinical practice lags behind their dissemination in the medical literature. Arden Syntax is a standard that could be used to encode evidence in a clinical decision support system (CDSS). However, dissemination of knowledge is hampered by lack of standard linkages to clinical databases. OBJECTIVE: To create a knowledge editor that facilitates transfer of knowledge from the medical literature to clinical practice via a CDSS. METHODS: Using a Web browser-based application, we implemented linkages to MEDLINE to permit queries on demand and registration of queries to be executed periodically, with results copied into Arden Medical Logic Modules (MLMs). To facilitate standardization of MLMs, database linkages are encoded using emerging HL7 standards such as a data model (virtual medical record). CONCLUSIONS: A Web-based application can facilitate transfer of knowledge into clinical practice and knowledge base maintenance through periodic queries and deployment of standards for knowledge representation.

Artificial Intelligence↗
PubMed1994

HYCONES II: a tool to build hybrid connectionist expert systems.

This paper describes HYCONES II--a tool to enable the construction of hybrid connectionist expert systems to solve classification problems. HYCONES II offers to the knowledge engineer a hybrid knowledge base that integrates frames with three different neural network models: the combinatorial neural model--CNM, the Fuzzy ARTMAP and the Semantic ART--SMART models. The latter is a new model, introduced by this paper, based on a combination of the two previous models. The validation section compares the performance of these three neural models to solve diagnostic problems in two medical domains. This paper also presents HYCONES II knowledge representation features, built in the symbolic component of its hybrid knowledge-base, to deal and represent fuzzy medical variables. Finally, the present status and future developments of the project are presented.

Algorithms↗
PubMed2003

Knowledge acquisition, consistency checking and concurrency control for Gene Ontology (GO).

MOTIVATION: A critical element of the computational infrastructure required for functional genomics is a shared language for communicating biological data and knowledge. The Gene Ontology (GO; http://www.geneontology.org) provides a taxonomy of concepts and their attributes for annotating gene products. As GO increases in size, its ongoing construction and maintenance becomes more challenging. In this paper, we assess the applicability of a Knowledge Base Management System (KBMS), Protégé-2000, to the maintenance and development of GO. RESULTS: We transferred GO to Protégé-2000 in order to evaluate its suitability for GO. The graphical user interface supported browsing and editing of GO. Tools for consistency checking identified minor inconsistencies in GO and opportunities to reduce redundancy in its representation. The Protégé Axiom Language proved useful for checking ontological consistency. The PROMPT tool allowed us to track changes to GO. Using Protégé-2000, we tested our ability to make changes and extensions to GO to refine the semantics of attributes and classify more concepts. AVAILABILITY: Gene Ontology in Protégé-2000 and the associated code are located at http://smi.stanford.edu/projects/helix/gokbms/. Protégé-2000 is available from http://protege.stanford.edu.

Artificial Intelligence↗
PubMed2005

Concept-based annotation of enzyme classes.

MOTIVATION: Given the explosive growth of biomedical data as well as the literature describing results and findings, it is getting increasingly difficult to keep up to date with new information. Keeping databases synchronized with current knowledge is a time-consuming and expensive task-one which can be alleviated by automatically gathering findings from the literature using linguistic approaches. We describe a method to automatically annotate enzyme classes with disease-related information extracted from the biomedical literature for inclusion in such a database. RESULTS: Enzyme names for the 3901 enzyme classes in the BRENDA database, a repository for quantitative and qualitative enzyme information, were identified in more than 100,000 abstracts retrieved from the PubMed literature database. Phrases in the abstracts were assigned to concepts from the Unified Medical Language System (UMLS) utilizing the MetaMap program, allowing for the identification of disease-related concepts by their semantic fields in the UMLS ontology. Assignments between enzyme classes and diseases were created based on their co-occurrence within a single sentence. False positives could be removed by a variety of filters including minimum number of co-occurrences, removal of sentences containing a negation and the classification of sentences based on their semantic fields by a Support Vector Machine. Verification of the assignments with a manually annotated set of 1500 sentences yielded favorable results of 92% precision at 50% recall, sufficient for inclusion in a high-quality database. AVAILABILITY: Source code is available from the author upon request. SUPPLEMENTARY INFORMATION: ftp.uni-koeln.de/institute/biochemie/pub/brenda/info/diseaseSupp.pdf.

Algorithms↗
PubMed1998

DXplain on the Internet.

DXplain, a computer-based medical education, reference and decision support system has been used by thousands of physicians and medical students on stand-alone systems and over communications networks. For the past two years, we have made DXplain available over the Internet in order to provide DXplain's knowledge and analytical capabilities as a resource to other applications within Massachusetts General Hospital (MGH) and at outside institutions. We describe and provide the user experience with two different protocols through which users can access DXplain through the World Wide Web (WWW). The first allows the user to have direct interaction with all the functionality of DXplain where the MGH server controls the interaction and the mode of presentation. In the second mode, the MGH server provides the DXplain functionality as a series of services, which can be called independently by the user application program.

Artificial Intelligence↗
PubMed2001

Building an explanation function for a hypertension decision-support system.

ATHENA DSS is a decision-support system that provides recommendations for managing hypertension in primary care. ATHENA DSS is built on a component-based architecture called EON. User acceptance of a system like this one depends partly on how well the system explains its reasoning and justifies its conclusions. We addressed this issue by adapting WOZ, a declarative explanation framework, to build an explanation function for ATHENA DSS. ATHENA DSS is built based on a component-based architecture called EON. The explanation function obtains its information by tapping into EON's components, as well as into other relevant sources such as the guideline document and medical literature. It uses an argument model to identify the pieces of information that constitute an explanation, and employs a set of visual clients to display that explanation. By incorporating varied information sources, by mirroring naturally occurring medical arguments and by utilizing graphic visualizations, ATHENA DSS's explanation function generates rich, evidence-based explanations.

Artificial Intelligence↗
PubMed2000

Design of a clinical alert system to facilitate development, testing, maintenance, and user-specific notification.

Creation and maintenance of electronic clinical alerts within a hospital's electronic medical record (EMR) or database poses a number of challenges. Development can require significant programming effort. Final testing should ideally be performed in a real clinical environment without clinician notification, which may create technical challenges. After an alert is in production, modifications may become necessary in response clinician feedback, changes in clinical factors, or technical issues. Changes may be required in the knowledge base utilized by the alert or in the presentation of the alert condition to the clinicians. Occasionally, different users within the clinical environment may wish to have the same alert data presented differently. We have developed a strategy which allows development of multi-functional alerts and facilitates modification of alert function and/or presentation with minimal to no programming effort. Some elements of this scheme may be appropriate for incorporation into clinical alerting standards.

Artificial Intelligence↗
PubMed2005

Individualizing generic decision models using assessments as evidence.

Complex decision models in expert systems often depend upon a number of utilities and subjective probabilities for an individual. Although these values can be estimated for entire populations or demographic subgroups, a model should be customized to the individual's specific parameter values. This process can be onerous and inefficient for practical decisions. We propose an interactive approach for incrementally improving our knowledge about a specific individual's parameter values, including utilities and probabilities, given a decision model and a prior joint probability distribution over the parameter values. We define the concept of value of elicitation and use it to determine dynamically the next most informative elicitation for a given individual. We evaluated the approach using an example model and demonstrate that we can improve the decision quality by focusing on those parameter values most material to the decision.

Algorithms↗
PubMed1997

New computer-based tools for empiric antibiotic decision support.

Since 1995 we have been developing a decision-support model, called Q-ID, which uses a series of infectious disease knowledge bases to make recommendations for empirical treatment or to check the appropriateness of current antibiotic therapy. From disease manifestations and risk factors, a differential diagnosis for the patient is generated by a diagnostic medical expert system. The resulting probability of each: disease is multiplied by the expected benefit in improved mortality and morbidity from optimal antibiotic treatment of each disease. To generate empirical treatment recommendations, site-specific data on sensitivity to antibiotics of each organism is used as an estimate of the likelihood of achieving maximum benefit for each disease on the patient's differential. Combining this data with drug and patient specific factors, the model recommends the antibiotic(s) most likely to produce the optimal benefit in this patient with the least risk and expense. In this paper the model is described, excerpts from each of the knowledge bases are presented, and performance of the model in a real case is shown for illustration.

Aged↗
PubMed1998

Evolution of a knowledge base for a clinical decision support system encoded in the Arden Syntax.

Clinical decision support systems (CDSS) are being used increasingly in medical practice. Thus, long-term maintenance of the knowledge bases (KB) of such systems becomes important. To quantify changes that occur as a KB evolves, we studied the KB at the Columbia-Presbyterian Medical Center. This KB has a total of 229 Medical Logic Modules (MLMs) encoded in the Arden Syntax. Eliminating those never used in practice, we retrospectively analyzed 156 MLMs developed over 78 months. We noted 2020 distinct versions of these MLMs that included 5528 changed statements over time. These changes occurred primarily in the logic slot (38.7% of all changes), the action slot (17.8%), in queries (15.0%) and in the data slot exclusive of queries (12.4%). We conclude that long-term maintenance of a KB for a CDSS requires significant changes over time. We discuss the implications of these results for the design of KB editors for the Arden Syntax.

Artificial Intelligence↗
PubMed2006

GOPET: a tool for automated predictions of Gene Ontology terms.

BACKGROUND: Vast progress in sequencing projects has called for annotation on a large scale. A Number of methods have been developed to address this challenging task. These methods, however, either apply to specific subsets, or their predictions are not formalised, or they do not provide precise confidence values for their predictions. DESCRIPTION: We recently established a learning system for automated annotation, trained with a broad variety of different organisms to predict the standardised annotation terms from Gene Ontology (GO). Now, this method has been made available to the public via our web-service GOPET (Gene Ontology term Prediction and Evaluation Tool). It supplies annotation for sequences of any organism. For each predicted term an appropriate confidence value is provided. The basic method had been developed for predicting molecular function GO-terms. It is now expanded to predict biological process terms. This web service is available via http://genius.embnet.dkfz-heidelberg.de/menu/biounit/open-husar CONCLUSION: Our web service gives experimental researchers as well as the bioinformatics community a valuable sequence annotation device. Additionally, GOPET also provides less significant annotation data which may serve as an extended discovery platform for the user.

Artificial Intelligence↗
PubMed1979

Silver-Russell syndrome. Observations in 20 patients.

The growth and development data of 20 patients with the Silver-Russell syndrome (14 boys, 6 girls) were analyzed. Family history, pregnancy and delivery did not reveal any significant anomalies. Birth length was 44.0 +/- 3.0 cm (boys) and 43.8 +/- 2.1 cm (girls), birth weight 2.0 +/- 0.4 kg and 2.05 +/- 0.3 kg, respectively. At the time of diagnosis (mean age 4.1 +/- 2.2 years), height was -4.4, bone age -1.9, weight -3.7, and head circumference -1.5 standard deviations below the normal mean for age. Calculated or reached adult height corresponded to 82--94% of target height. Intelligence was normal in most patients. 8 had asymmetrical extremities, 3 an asymmetrical face. 7 of 14 boys had cryptorchidism (3 uni-, 4 bilateral), 2 incomplete masculinization, and 2 of 6 girls hypertrophy of the clitoris. Development of secondary sex characters was appropriate for bone age with exception of one boy, whose puberty was early. In 3 boys with completed pubertal development, testicular volume was small and gonadotropins (before and after LHRH) high. It is concluded that 1. the growth pattern in Silver-Russell syndrome is quite homogeneous, and rather accurate predictions are possible; 2. Intersexual genitalia do not seem to be related to endocrine factors, and 3. hypergonadotropic hypogonadism appears to be frequent in males.

Abnormalities, Multiple↗
PubMed2005

Co-occurrence based meta-analysis of scientific texts: retrieving biological relationships between genes.

MOTIVATION: The advent of high-throughput experiments in molecular biology creates a need for methods to efficiently extract and use information for large numbers of genes. Recently, the associative concept space (ACS) has been developed for the representation of information extracted from biomedical literature. The ACS is a Euclidean space in which thesaurus concepts are positioned and the distances between concepts indicates their relatedness. The ACS uses co-occurrence of concepts as a source of information. In this paper we evaluate how well the system can retrieve functionally related genes and we compare its performance with a simple gene co-occurrence method. RESULTS: To assess the performance of the ACS we composed a test set of five groups of functionally related genes. With the ACS good scores were obtained for four of the five groups. When compared to the gene co-occurrence method, the ACS is capable of revealing more functional biological relations and can achieve results with less literature available per gene. Hierarchical clustering was performed on the ACS output, as a potential aid to users, and was found to provide useful clusters. Our results suggest that the algorithm can be of value for researchers studying large numbers of genes. AVAILABILITY: The ACS program is available upon request from the authors.

Artificial Intelligence↗