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Information: needs for the future.

The four central questions surrounding the use of information are: where to find it; how to find it; how to present it; and how to maintain information availability and information literacy. It is usually assumed that the main source of information for most scientists is the peer-reviewed journal literature. Traditional journal publishing is beset with a number of problems. Although electronic publishing might possibly solve some of these, it in turn introduces new problems. Further problems arise with respect to secondary sources which, in some cases, are being supplemented by electronic archives of full-text documents. One fundamental question that arises when considering any large collection of documents or of records about documents is whether or not to index them, and how to index them. The pros and cons of free-text searching versus the use of controlled vocabularies are discussed, as is the importance of harmonising the Three Rs-related terminology of existing and proposed thesauri. However, there is a further problem that documents pertinent to the Three Rs are not always indexed from this point of view. Authors need to be made aware that, if the information is not provided in the abstract, there is no easy way to identify and retrieve this document from a database. Small specialised databases on the Three Rs in relation to specific subject areas could provide a further solution, especially if they provide references to conference proceedings and book chapters, which are not usually found in the large bibliographical databases. The provision of training in the use of information resources, and the establishment and maintenance of these resources require investment of money and professional skills. Finally, the future of Three Rs information depends on a recognition that this in an important topic which deserves more than lip service.

Animal Testing Alternatives↗

Combining evidence, biomedical literature and statistical dependence: new insights for functional annotation of gene sets.

BACKGROUND: Large-scale genomic studies based on transcriptome technologies provide clusters of genes that need to be functionally annotated. The Gene Ontology (GO) implements a controlled vocabulary organised into three hierarchies: cellular components, molecular functions and biological processes. This terminology allows a coherent and consistent description of the knowledge about gene functions. The GO terms related to genes come primarily from semi-automatic annotations made by trained biologists (annotation based on evidence) or text-mining of the published scientific literature (literature profiling). RESULTS: We report an original functional annotation method based on a combination of evidence and literature that overcomes the weaknesses and the limitations of each approach. It relies on the Gene Ontology Annotation database (GOA Human) and the PubGene biomedical literature index. We support these annotations with statistically associated GO terms and retrieve associative relations across the three GO hierarchies to emphasise the major pathways involved by a gene cluster. Both annotation methods and associative relations were quantitatively evaluated with a reference set of 7397 genes and a multi-cluster study of 14 clusters. We also validated the biological appropriateness of our hybrid method with the annotation of a single gene (cdc2) and that of a down-regulated cluster of 37 genes identified by a transcriptome study of an in vitro enterocyte differentiation model (CaCo-2 cells). CONCLUSION: The combination of both approaches is more informative than either separate approach: literature mining can enrich an annotation based only on evidence. Text-mining of the literature can also find valuable associated MEDLINE references that confirm the relevance of the annotation. Eventually, GO terms networks can be built with associative relations in order to highlight cooperative and competitive pathways and their connected molecular functions.

Algorithms↗

Identifying biological concepts from a protein-related corpus with a probabilistic topic model.

BACKGROUND: Biomedical literature, e.g., MEDLINE, contains a wealth of knowledge regarding functions of proteins. Major recurring biological concepts within such text corpora represent the domains of this body of knowledge. The goal of this research is to identify the major biological topics/concepts from a corpus of protein-related MEDLINE titles and abstracts by applying a probabilistic topic model. RESULTS: The latent Dirichlet allocation (LDA) model was applied to the corpus. Based on the Bayesian model selection, 300 major topics were extracted from the corpus. The majority of identified topics/concepts was found to be semantically coherent and most represented biological objects or concepts. The identified topics/concepts were further mapped to the controlled vocabulary of the Gene Ontology (GO) terms based on mutual information. CONCLUSION: The major and recurring biological concepts within a collection of MEDLINE documents can be extracted by the LDA model. The identified topics/concepts provide parsimonious and semantically-enriched representation of the texts in a semantic space with reduced dimensionality and can be used to index text.

Abstracting and Indexing↗

Mapping data elements to terminological resources for integrating biomedical data sources.

BACKGROUND: Data integration is a crucial task in the biomedical domain and integrating data sources is one approach to integrating data. Data elements (DEs) in particular play an important role in data integration. We combine schema- and instance-based approaches to mapping DEs to terminological resources in order to facilitate data sources integration. METHODS: We extracted DEs from eleven disparate biomedical sources. We compared these DEs to concepts and/or terms in biomedical controlled vocabularies and to reference DEs. We also exploited DE values to disambiguate underspecified DEs and to identify additional mappings. RESULTS: 82.5% of the 474 DEs studied are mapped to entries of a terminological resource and 74.7% of the whole set can be associated with reference DEs. Only 6.6% of the DEs had values that could be semantically typed. CONCLUSION: Our study suggests that the integration of biomedical sources can be achieved automatically with limited precision and largely facilitated by mapping DEs to terminological resources.

Abstracting and Indexing↗

The Sequence Ontology: a tool for the unification of genome annotations.

The Sequence Ontology (SO) is a structured controlled vocabulary for the parts of a genomic annotation. SO provides a common set of terms and definitions that will facilitate the exchange, analysis and management of genomic data. Because SO treats part-whole relationships rigorously, data described with it can become substrates for automated reasoning, and instances of sequence features described by the SO can be subjected to a group of logical operations termed extensional mereology operators.

Alternative Splicing↗

Achieving evolvable Web-database bioscience applications using the EAV/CR framework: recent advances.

The EAV/CR framework, designed for database support of rapidly evolving scientific domains, utilizes metadata to facilitate schema maintenance and automatic generation of Web-enabled browsing interfaces to the data. EAV/CR is used in SenseLab, a neuroscience database that is part of the national Human Brain Project. This report describes various enhancements to the framework. These include (1) the ability to create "portals" that present different subsets of the schema to users with a particular research focus, (2) a generic XML-based protocol to assist data extraction and population of the database by external agents, (3) a limited form of ad hoc data query, and (4) semantic descriptors for interclass relationships and links to controlled vocabularies such as the UMLS.

Database Management Systems↗

Galen-In-Use: using artificial intelligence terminology tools to improve the linguistic coherence of a national coding system for surgical procedures.

GALEN has developed a language independent common reference model based on a medically oriented ontology and practical tools and techniques for managing healthcare terminology including natural language processing. GALEN-IN-USE is the current phase which applied the modelling and the tools to the development or the updating of coding systems for surgical procedures in different national coding centers co-operating within the European Federation of Coding Centre (EFCC) to create a language independent knowledge repository for multicultural Europe. We used an integrated set of artificial intelligence terminology tools named CLAssification Manager workbench to process French professional medical language rubrics into intermediate dissections and to the Grail reference ontology model representation. From this language independent concept model representation we generate controlled French natural language. The French national coding centre is then able to retrieve the initial professional rubrics with different categories of concepts, to compare the professional language proposed by expert clinicians to the French generated controlled vocabulary and to finalize the linguistic labels of the coding system in relation with the meanings of the conceptual system structure.

Artificial Intelligence↗

GIN AUSTRIA. Assuring quality and relevance on Internet-health-informations for patients.

GIN AUSTRIA (Gesundheitsinformationsnetz AUSTRIA) offers patients and consumers reliable medical knowledge about diseases, wellness and disease management in an understandable way and enables them to quick and incessant access to informations about the Austrian health system and Austrian health organizations. To improve the quality of the database and to achieve full customer (patients, citizens) satisfaction a systematic approach for implementing total quality management is also applied. Focusing the attention on understanding and responding to customer needs, systematic and continuous improving of the IS and total involvement of all participants are the three core TQM principles at this project. The second focus of the project is the development and the implementation (prototype) of a medical dictionary or rather medical thesaurus as interface for patients, who are not used to scientific terms and expressions. This interface is based on the controlled vocabulary of the MeSH-Thesaurus (german version).

Austria↗

Expanding DIOGENE with a clinical information system based on a new hospital-wide clinical finding dictionary.

The aim of this project is to expand DIOGENE with a centralized and integrated patient clinical database system providing a standardized framework for the building of future clinical databases and for the integration of existing heterogeneous ones. The combined 'across time view' and 'across departments view' generated from the integrated clinical data will enable an evolutionary view of the patient state both across time and across medical specialties. For this purpose and to permit the communication and exchange of data, a new controlled vocabulary for representing clinical data has been created. The construction of this vocabulary is based on the international ICD classification, already being used in DIOGENE for encoding patient diagnosis and procedures. A new extension of the ICD is proposed for medical information that goes beyond diagnosis and procedures encoding. The building of a common clinical finding dictionary recording the definition of findings is based on this newly developed clinical vocabulary. This process is incremental, manual, and significant.

Hospital Information Systems↗

Using semantic distance for the efficient coding of medical concepts.

OBJECTIVE: To use the notion of semantic distance to find the nearest neighbors of a medical concept in a controlled vocabulary. MATERIAL AND METHOD: 392 concepts from the cardiovascular chapter of the ICD-10 were projected on the axes of SNOMED III. Distances were measured on each axis and the resulting distance was found using a Lp norm. RESULTS: The distance between a set of ischemic diseases and a set of non-ischemic diseases was significant (p < 0.0001). Our method was validated by finding the k nearest neighbors of ten different diagnoses from the ICD-10 cardiovascular chapter. DISCUSSION: The availability of SNOMED-RT should improve our method. Several more steps are necessary to provide an ideal coding tool.

Cardiovascular Diseases↗

Family practice clerkship encounters documented with structured phrases on paper and hand-held computer logs.

Patient encounter logs allow faculty to monitor students' clinical experiences, especially in decentralized clerkships. However, there are generally tradeoffs involving the expressiveness of patient encounter forms, the effort required to complete the forms, and the utility of the forms for informing the clerkship director. The family practice clerkship at Washington University changed the school's standard free text, paper log to a controlled vocabulary paper log, borrowing 93 generic ICD-9 codes and the SNOMED concept of 'process at location' phrases for localized problems. Subsequently, this architecture was used in a Palm computer program. Students using the structured paper logs documented slightly more patient encounters than students using free text logs in the previous year, with similar numbers of problems per patient (1.3 to 1.4) and prevalence of common illnesses, but used the phrase structure and code vocabulary inconsistently. Students using computer logs documented many more patient encounters, but only documented 1.09 problems per patient. Students' documentation of psychosocial diagnoses declined significantly with the computer log. Although the computer program was flexible, the effort required to enter multiple problems exceeded the effort of finding similar codes on a short paper form. This problem confounds efforts to monitor exposure to complex patients and hidden medical problems. Another design for the hand-held computer log is being tested.

Clinical Clerkship↗

A MeSH based intelligent search intermediary for consumer health information systems.

GIN AUSTRIA (Gesundheitsinformationsnetz AUSTRIA) offers patients and consumers reliable medical knowledge about diseases, wellness and disease management in an easy understandable way and enables them to quick and incessant access to informations about the Austrian health system and Austrian health organizations. To achieve full customer (patients, citizens) satisfaction to find relevant information we propose a concept of a vector-model oriented IR-Tool based on the controlled vocabulary of the MeSH Thesaurus (german version). By this approach users who are often not used to scientific terms and expressions are supported to build up their own query with MeSH Main Headings. In a second step broader and narrower Main Headings are added to the query-vector by the system. The tool is part of the GIN Search Modul, which will ease gathering health information from different heterogenous internet datasources.

Austria↗

Can we classify medical data dictionaries?

Medical Data Dictionaries enable a clinical information system to maintain a controlled vocabulary, to store descriptive knowledge about terms, to map between those terms and from those terms to external classifications. They support a variety of functions in the information system, ranging from structured documentation to knowledgebased functions. This paper derives a multi-axial classification for medical data dictionaries. Dictionaries are classified along 4 axes, a vocabulary axis defining vocabulary properties, an application axis which characterises the degree of linkage between dictionary and information system, a semantic axis defining the quality of inter-term relationships and finally a language axis which classifies rules for inter-term relationships in semiotic theory. As an example two existing dictionaries are classified in the model and reference is taken to the design of future dictionaries.

Artificial Intelligence↗

Decision support for infectious diseases--a working prototype.

This paper presents a decision support system for nosocomial infections and its integration in the large HIS of the University Hospital of Giessen. The model comprises five different engines and a data dictionary. It is designed to detect hospital acquired infections even in a situation where only a restricted amount of clinical data is available (the data is split up in different information systems). Furthermore the model prevents time consuming manual data entry. The five engines split the main task into 1) a preselection, which sort out patients who definitely do not have a nosocomial infection; 2) a rule based reasoning process which detects patients likely to have such an infection; 3) an alarm process which is responsible for the presentation of the alert; 4) an explanation process to follow up the reasoning and 5) statistic tools to answer specific hygienic questions. A data dictionary supplies the controlled vocabulary, but it is also required to understand datastructures used in the different clinical subsystems.

Artificial Intelligence↗

A multilingual medical thesaurus browser for patients and medical content managers.

This paper introduces a user-friendly browser interface which integrates multilingual search and browsing functionalities within medical thesauri via the internet. The tool is being developed as part of the GIN Austria Patient Information System and is based on an adapted datamodel of the MeSH thesaurus. A prototype offers the possibility to build up queries and export lists of MeSH main headings collected during browsing the relevant MeSH trees. The thesaurus browser can be used both by patients and citizens to build queries based on a controlled vocabulary to match them with existing documents within GIN and by medical information managers to find out appropriate keywords for interactive tagging or indexing of medical contents. A key component of this tool is the flexible choice of different languages of the MeSH datasource as well as of the user interface. Both can be changed independently at any point during a session. Another central aspect is the use of the UMLS Metathesaurus in combination with localized Thesaurus versions due to existing international character set problems.

Abstracting and Indexing↗

A light knowledge model for linguistic applications.

Content extraction from medical texts is achievable today by linguistic applications, in so far as sufficient domain knowledge is available. Such knowledge represents a model of the domain and is hard to collect with sufficient depth and good coverage, despite numerous attempts. To leverage this task is a priority in order to benefit from the awaited linguistic tools. The light model is designed with this goal in mind. Syntactic and lexical information are generally available with large lexicons. A domain model should add the necessary semantic information. The authors have designed a light knowledge model for the collection of semantic information on the basis of the recognized syntactical and lexical attributes. It has been tailored for the acquisition of enough semantic information in order to retrieve terms of a controlled vocabulary from free texts, as for example, to retrieve Mesh terms from patient records.

Information Storage and Retrieval↗

Dental concepts in the Unified Medical Language System.

OBJECTIVE: Currently, no comprehensive, controlled vocabulary for dentistry is available. The objective of this study was to determine how well the Unified Medical Language System, the largest repository of concepts and terms in biomedicine, represents dental concepts. METHOD AND MATERIALS: The dental subset of concepts was extracted from Unified Medical Language System using the software program APEX (APplication for the EXtraction of domain-specific concepts). The relationships contained in the Unified Medical Language System Metathesaurus were used to locate the concepts related to 12 seed terms. The Encyclopédie Médico-Chirurgicale (513 dental terms) and the Diagnostic Codes developed by Leake et al (124 terms) were compared to the dental subset. Terms were classified as exact match, related term, or no match. The resultant matching characteristics were compared to those determined by the National Library of Medicine/Agency for Health Care Policy and Research Large Scale Vocabulary Test. RESULTS: The dental subset of Unified Medical Language System contained 948 concepts. The Encyclopédie Médico-Chirurgicale and the Diagnostic Codes exhibited similar matching characteristics for exact match (61.6% and 58.9%, respectively) and related term (38.0% and 32.2%, respectively). For no match, the matching frequencies were significantly different (P < .001). CONCLUSION: The Unified Medical Language System may be a comprehensive source of terms suitable for various representation requirements in dentistry.

Chi-Square Distribution↗

Towards a unified medical lexicon for French.

Medical Informatics has a constant need for basic Medical Language Processing tasks, e.g., for coding into controlled vocabularies, free text indexing and information retrieval. Most of these tasks involve term matching and rely on lexical resources: lists of words with attached information, including inflected forms and derived words, etc. Such resources are publicly available for the English language with the UMLS Specialist Lexicon, but not in other languages. For the French language, several teams have worked on the subject and built local lexical resources. The goal of the present work is to pool and unify these resources and to add extensively to them by exploiting medical terminologies and corpora, resulting in a unified medical lexicon for French (UMLF). This paper exposes the issues raised by such an objective, describes the methods on which the project relies and illustrates them with experimental results.

Algorithms↗