There is an increasing interest in using literature mining techniques to complement information extracted from annotation databases or generated by bioinformatics applications. Here we present PLAN2L, a web-based online search system that integrates text mining and information extraction techniques to access systematically information useful for analyzing genetic, cellular and molecular aspects of the plant model organism Arabidopsis thaliana. Our system facilitates a more efficient retrieval of information relevant to heterogeneous biological topics, from implications in biological relationships at the level of protein interactions and gene regulation, to sub-cellular locations of gene products and associations to cellular and developmental processes, i.e. cell cycle, flowering, root, leaf and seed development. Beyond single entities, also predefined pairs of entities can be provided as queries for which literature-derived relations together with textual evidences are returned. PLAN2L does not require registration and is freely accessible at http://zope.bioinfo.cnio.es/plan2l.
Over the last few decades, the ever-increasing output of scientific publications has led to new challenges to keep up to date with the literature. In the biomedical area, this growth has introduced new requirements for professionals, e.g., physicians, who have to locate the exact papers that they need for their clinical and research work amongst a huge number of publications. Against this backdrop, novel information retrieval methods are even more necessary. While web search engines are widespread in many areas, facilitating access to all kinds of information, additional tools are required to automatically link information retrieved from these engines to specific biomedical applications. In the case of clinical environments, this also means considering aspects such as patient data security and confidentiality or structured contents, e.g., electronic health records (EHRs). In this scenario, we have developed a new tool to facilitate query building to retrieve scientific literature related to EHRs.
We have developed CDAPubMed, an open-source web browser extension to integrate EHR features in biomedical literature retrieval approaches. Clinical users can use CDAPubMed to: (i) load patient clinical documents, i.e., EHRs based on the Health Level 7-Clinical Document Architecture Standard (HL7-CDA), (ii) identify relevant terms for scientific literature search in these documents, i.e., Medical Subject Headings (MeSH), automatically driven by the CDAPubMed configuration, which advanced users can optimize to adapt to each specific situation, and (iii) generate and launch literature search queries to a major search engine, i.e., PubMed, to retrieve citations related to the EHR under examination.
CDAPubMed is a platform-independent tool designed to facilitate literature searching using keywords contained in specific EHRs. CDAPubMed is visually integrated, as an extension of a widespread web browser, within the standard PubMed interface. It has been tested on a public dataset of HL7-CDA documents, returning significantly fewer citations since queries are focused on characteristics identified within the EHR. For instance, compared with more than 200,000 citations retrieved by breast neoplasm, fewer than ten citations were retrieved when ten patient features were added using CDAPubMed. This is an open source tool that can be freely used for non-profit purposes and integrated with other existing systems.
Acquiring and representing biomedical knowledge is an increasingly important component of contemporary bioinformatics. A critical step of the process is to identify and retrieve relevant documents among the vast volume of modern biomedical literature efficiently. In the real world, many information retrieval tasks are difficult because of high data dimensionality and the lack of annotated examples to train a retrieval algorithm. Under such a scenario, the performance of information retrieval algorithms is often unsatisfactory, therefore improvements are needed.
We studied two approaches that enhance the text categorization performance on sparse and high data dimensionality: (1) semantic-preserving dimension reduction by representing text with semantic-enriched features; and (2) augmenting training data with semi-supervised learning. A probabilistic topic model was applied to extract major semantic topics from a corpus of text of interest. The representation of documents was projected from the high-dimensional vocabulary space onto a semantic topic space with reduced dimensionality. A semi-supervised learning algorithm based on graph theory was applied to identify potential positive training cases, which were further used to augment training data. The effects of data transformation and augmentation on text categorization by support vector machine (SVM) were evaluated.
Results and Conclusion
Semantic-enriched data transformation and the pseudo-positive-cases augmented training data enhance the efficiency and performance of text categorization by SVM.
Searching the Web for documents using information retrieval systems plays an important part in clinicians’ practice of evidence-based medicine. While much research focuses on the design of methods to retrieve documents, there has been little examination of the way different search engine capabilities influence clinician search behaviors.
Previous studies have shown that use of task-based search engines allows for faster searches with no loss of decision accuracy compared with resource-based engines. We hypothesized that changes in search behaviors may explain these differences.
In all, 75 clinicians (44 doctors and 31 clinical nurse consultants) were randomized to use either a resource-based or a task-based version of a clinical information retrieval system to answer questions about 8 clinical scenarios in a controlled setting in a university computer laboratory. Clinicians using the resource-based system could select 1 of 6 resources, such as PubMed; clinicians using the task-based system could select 1 of 6 clinical tasks, such as diagnosis. Clinicians in both systems could reformulate search queries. System logs unobtrusively capturing clinicians’ interactions with the systems were coded and analyzed for clinicians’ search actions and query reformulation strategies.
The most frequent search action of clinicians using the resource-based system was to explore a new resource with the same query, that is, these clinicians exhibited a “breadth-first” search behaviour. Of 1398 search actions, clinicians using the resource-based system conducted 401 (28.7%, 95% confidence interval [CI] 26.37-31.11) in this way. In contrast, the majority of clinicians using the task-based system exhibited a “depth-first” search behavior in which they reformulated query keywords while keeping to the same task profiles. Of 585 search actions conducted by clinicians using the task-based system, 379 (64.8%, 95% CI 60.83-68.55) were conducted in this way.
This study provides evidence that different search engine designs are associated with different user search behaviors.
Clinician; search behavior; information retrieval; Internet
Because of the increasing number of electronic resources, designing efficient tools to retrieve and exploit them is a major challenge. Some improvements have been offered by semantic Web technologies and applications based on domain ontologies. In life science, for instance, the Gene Ontology is widely exploited in genomic applications and the Medical Subject Headings is the basis of biomedical publications indexation and information retrieval process proposed by PubMed. However current search engines suffer from two main drawbacks: there is limited user interaction with the list of retrieved resources and no explanation for their adequacy to the query is provided. Users may thus be confused by the selection and have no idea on how to adapt their queries so that the results match their expectations.
This paper describes an information retrieval system that relies on domain ontology to widen the set of relevant documents that is retrieved and that uses a graphical rendering of query results to favor user interactions. Semantic proximities between ontology concepts and aggregating models are used to assess documents adequacy with respect to a query. The selection of documents is displayed in a semantic map to provide graphical indications that make explicit to what extent they match the user's query; this man/machine interface favors a more interactive and iterative exploration of data corpus, by facilitating query concepts weighting and visual explanation. We illustrate the benefit of using this information retrieval system on two case studies one of which aiming at collecting human genes related to transcription factors involved in hemopoiesis pathway.
The ontology based information retrieval system described in this paper (OBIRS) is freely available at: http://www.ontotoolkit.mines-ales.fr/ObirsClient/. This environment is a first step towards a user centred application in which the system enlightens relevant information to provide decision help.
A wealth of genomic information is available in public and private databases. However, this information is underutilized for uncovering population specific and functionally relevant markers underlying complex human traits. Given the huge amount of SNP data available from the annotation of human genetic variation, data mining is a faster and cost effective approach for investigating the number of SNPs that are informative for ancestry. In this study, we present AncestrySNPminer, the first web-based bioinformatics tool specifically designed to retrieve Ancestry Informative Markers (AIMs) from genomic data sets and link these informative markers to genes and ontological annotation classes. The tool includes an automated and simple “scripting at the click of a button” functionality that enables researchers to perform various population genomics statistical analyses methods with user friendly querying and filtering of data sets across various populations through a single web interface. AncestrySNPminer can be freely accessed at https://research.cchmc.org/mershalab/AncestrySNPminer/login.php.
Ancestry; Ancestry informative markers; AIMs; Bioinformatics; AncestrySNPminer; Data mining; Admixture; Admixture mapping
The biomedical literature is represented by millions of abstracts available in the Medline database. These abstracts can be queried with the PubMed interface, which provides a keyword-based Boolean search engine. This approach shows limitations in the retrieval of abstracts related to very specific topics, as it is difficult for a non-expert user to find all of the most relevant keywords related to a biomedical topic. Additionally, when searching for more general topics, the same approach may return hundreds of unranked references. To address these issues, text mining tools have been developed to help scientists focus on relevant abstracts. We have implemented the MedlineRanker webserver, which allows a flexible ranking of Medline for a topic of interest without expert knowledge. Given some abstracts related to a topic, the program deduces automatically the most discriminative words in comparison to a random selection. These words are used to score other abstracts, including those from not yet annotated recent publications, which can be then ranked by relevance. We show that our tool can be highly accurate and that it is able to process millions of abstracts in a practical amount of time. MedlineRanker is free for use and is available at http://cbdm.mdc-berlin.de/tools/medlineranker.
The rapid growth of online publications such as the Medline and other sources raises the questions how to get the relevant information efficiently. It is important, for a bench scientist, e.g., to monitor related publications constantly. It is also important, for a clinician, e.g., to access the patient records anywhere and anytime. Although time-consuming, this kind of searching procedure is usually similar and simple. Likely, it involves a search engine and a visualization interface. Different words or combination reflects different research topics. The objective of this study is to automate this tedious procedure by recording those words/terms in a database and online sources, and use the information for an automated search and retrieval. The retrieved information will be available anytime and anywhere through a secure web server.
We developed such a database that stored searching terms, journals and et al., and implement a piece of software for searching the medical subject heading-indexed sources such as the Medline and other online sources automatically. The returned information were stored locally, as is, on a server and visible through a Web-based interface. The search was performed daily or otherwise scheduled and the users logon to the website anytime without typing any words. The system has potentials to retrieve similarly from non-medical subject heading-indexed literature or a privileged information source such as a clinical information system. The issues such as security, presentation and visualization of the retrieved information were thus addressed. One of the presentation issues such as wireless access was also experimented. A user survey showed that the personalized online searches saved time and increased and relevancy. Handheld devices could also be used to access the stored information but less satisfactory.
The Web-searching software or similar system has potential to be an efficient tool for both bench scientists and clinicians for their daily information needs.
To bridge the gap between the rising information needs of biological and medical researchers and the rapidly growing number of online bioinformatics resources, we have created the Online Bioinformatics Resources Collection (OBRC) at the Health Sciences Library System (HSLS) at the University of Pittsburgh. The OBRC, containing 1542 major online bioinformatics databases and software tools, was constructed using the HSLS content management system built on the Zope® Web application server. To enhance the output of search results, we further implemented the Vivísimo Clustering Engine®, which automatically organizes the search results into categories created dynamically based on the textual information of the retrieved records. As the largest online collection of its kind and the only one with advanced search results clustering, OBRC is aimed at becoming a one-stop guided information gateway to the major bioinformatics databases and software tools on the Web. OBRC is available at the University of Pittsburgh's HSLS Web site ().
This paper aims to retrieve and evaluate the quality of the Internet sites providing information on cardiovascular risk. We searched web pages related to risk prediction using six search engines. Sites proposing a cardiovascular risk prediction were selected for evaluation. The quality of each site was checked against criteria testing the validity, type and potential usefulness of information for physicians or patients. Search engines retrieved about 50 10(6) web pages. Eight sites were included. Only 2 of them provided calculation of cardiovascular risk based on Framingham equation. The others proposed algorithms, guidelines, or general information on cardiovascular health. Most sites lacked details to ensure quality of information. Present search engines are inefficient to retrieve precise and valid information. Facing the inflation of medical information, a systematic approach to validate the quality of a site is mandatory. Application of Evidence Based Medicine concepts gives a solution for evaluation of internet-based medical information.
Whether or not the hippocampus participates in semantic memory retrieval has been the focus of much debate in the literature. However, few neuroimaging studies have directly compared hippocampal activation during semantic and episodic retrieval tasks that are well matched in all respects other than the source of the retrieved information. In Experiment 1, we compared hippocampal fMRI activation during a classic semantic memory task, category production, and an episodic version of the same task, category cued recall. Left hippocampal activation was observed in both episodic and semantic conditions, although other regions of the brain clearly distinguished the two tasks. Interestingly, participants reported using retrieval strategies during the semantic retrieval task that relied on autobiographical and spatial information; for example, visualizing themselves in their kitchen while producing items for the category kitchen utensils. In Experiment 2, we considered whether the use of these spatial and autobiographical retrieval strategies could have accounted for the hippocampal activation observed in Experiment 1. Categories were presented that elicited one of three retrieval strategy types, autobiographical and spatial, autobiographical and nonspatial, and neither autobiographical nor spatial. Once again, similar hippocampal activation was observed for all three category types, regardless of the inclusion of spatial or autobiographical content. We conclude that the distinction between semantic and episodic memory is more complex than classic memory models suggest.
hippocampus; semantic memory; category production; episodic memory; fMRI; verbal fluency
Evaluating the biomedical literature and health-related websites for quality are challenging information retrieval tasks. Current commonly used methods include impact factor for journals, PubMed’s clinical query filters and machine learning-based filter models for articles, and PageRank for websites. Previous work has focused on the average performance of these methods without considering the topic, and it is unknown how performance varies for specific topics or focused searches. Clinicians, researchers, and users should be aware when expected performance is not achieved for specific topics. The present work analyzes the behavior of these methods for a variety of topics. Impact factor, clinical query filters, and PageRank vary widely across different topics while a topic-specific impact factor and machine learning-based filter models are more stable. The results demonstrate that a method may perform excellently on average but struggle when used on a number of narrower topics. Topic adjusted metrics and other topic robust methods have an advantage in such situations. Users of traditional topic-sensitive metrics should be aware of their limitations.
information retrieval; machine learning; PageRank; Journal Impact Factor; topic-sensitivity; bibliometrics
Due to language barrier, non-English users are unable to retrieve the most
updated medical information from the U.S. authoritative medical websites, such
as PubMed and MedlinePlus. A few cross-language medical information
retrieval (CLMIR) systems have been utilizing MeSH (Medical
Subject Heading) with multilingual thesaurus to bridge the gap. Unfortunately, MeSH
has yet not been translated into traditional Chinese currently.
We proposed a semi-automatic approach to constructing Chinese-English MeSH
based on Web-based term translation. The system provides knowledge
engineers with candidate terms mined from anchor texts and search-result
pages. The result is encouraging. Currently, more than 19,000 Chinese-English
MeSH entries have been compiled. This thesaurus will be used
in Chinese-English CLMIR in the future.
This informal tutorial is intended for investigators and students who would like to understand the workings of information retrieval systems, including the most frequently used search engines: PubMed and Google. Having a basic knowledge of the terms and concepts of information retrieval should improve the efficiency and productivity of searches. As well, this knowledge is needed in order to follow current research efforts in biomedical information retrieval and text mining that are developing new systems not only for finding documents on a given topic, but extracting and integrating knowledge across documents.
OBJECTIVE: To explain differences in the results of literature searches in British general practice and North American family practice or family medicine. DESIGN: Comparative literature search. SETTING: The Department of Family and Community Medicine at the University of Toronto in Ontario. METHOD: Literature searches on MEDLINE demonstrated that certain search strategies ignored certain key words, depending on the search engine and the search terms chosen. Literature searches using the key words "general practice," "family practice," and "family medicine" combined with the topics "depression" and then "otitis media" were conducted in MEDLINE using four different Web-based search engines: Ovid, HealthGate, PubMed, and Internet Grateful Med. MAIN OUTCOME MEASURES: The number of MEDLINE references retrieved for both topics when searched with each of the three key words, "general practice," "family practice," and "family medicine" using each of the four search engines. RESULTS: For each topic, each search yielded very different articles. Some search engines did a better job of matching the term "general practice" to the terms "family medicine" and "family practice," and thus improved retrieval. The problem of language use extends to the variable use of terminology and differences in spelling between British and American English. CONCLUSION: We need to heighten awareness of literature search problems and the potential for duplication of research effort when some of the literature is ignored, and to suggest ways to overcome the deficiencies of the various search engines.
One challenge presented by large-scale genome sequencing efforts
is effective display of uniform information to the scientific community.
The Comprehensive Microbial Resource (CMR) contains robust annotation
of all complete microbial genomes and allows for a wide variety
of data retrievals. The bacterial information has been placed on
the Web at http://www.tigr.org/CMR for
retrieval using standard web browsing technology. Retrievals can
be based on protein properties such as molecular weight or hydrophobicity,
GC-content, functional role assignments and taxonomy. The CMR also
has special web-based tools to allow data mining using pre-run homology
searches, whole genome dot-plots, batch downloading and traversal
across genomes using a variety of datatypes.
The biomedical literature is the primary information source for manual protein-protein interaction annotations. Text-mining systems have been implemented to extract binary protein interactions from articles, but a comprehensive comparison between the different techniques as well as with manual curation was missing.
We designed a community challenge, the BioCreative II protein-protein interaction (PPI) task, based on the main steps of a manual protein interaction annotation workflow. It was structured into four distinct subtasks related to: (a) detection of protein interaction-relevant articles; (b) extraction and normalization of protein interaction pairs; (c) retrieval of the interaction detection methods used; and (d) retrieval of actual text passages that provide evidence for protein interactions. A total of 26 teams submitted runs for at least one of the proposed subtasks. In the interaction article detection subtask, the top scoring team reached an F-score of 0.78. In the interaction pair extraction and mapping to SwissProt, a precision of 0.37 (with recall of 0.33) was obtained. For associating articles with an experimental interaction detection method, an F-score of 0.65 was achieved. As for the retrieval of the PPI passages best summarizing a given protein interaction in full-text articles, 19% of the submissions returned by one of the runs corresponded to curator-selected sentences. Curators extracted only the passages that best summarized a given interaction, implying that many of the automatically extracted ones could contain interaction information but did not correspond to the most informative sentences.
The BioCreative II PPI task is the first attempt to compare the performance of text-mining tools specific for each of the basic steps of the PPI extraction pipeline. The challenges identified range from problems in full-text format conversion of articles to difficulties in detecting interactor protein pairs and then linking them to their database records. Some limitations were also encountered when using a single (and possibly incomplete) reference database for protein normalization or when limiting search for interactor proteins to co-occurrence within a single sentence, when a mention might span neighboring sentences. Finally, distinguishing between novel, experimentally verified interactions (annotation relevant) and previously known interactions adds additional complexity to these tasks.
The new release of SchistoDB (http://SchistoDB.net) provides a rich resource of genomic data for key blood flukes (genus Schistosoma) which cause disease in hundreds of millions of people worldwide. SchistoDB integrates whole-genome sequence and annotation of three species of the genus and provides enhanced bioinformatics analyses and data-mining tools. A simple, yet comprehensive web interface provided through the Strategies Web Development Kit is available for the mining and visualization of the data. Genomic scale data can be queried based on BLAST searches, annotation keywords and gene ID searches, gene ontology terms, sequence motifs, protein characteristics and phylogenetic relationships. Search strategies can be saved within a user’s profile for future retrieval and may also be shared with other researchers using a unique web address.
QSCOP is a quantitative structural classification of proteins which distinguishes itself from other classifications by two essential properties: (i) QSCOP is concurrent with the Research Collaboratory for Structural Bioinformatics (RCSB) Protein Data Bank and (ii) QSCOP covers the widely used SCOP classification with layers of quantitative structural information. The QSCOP-BLAST web server presented here combines the BLAST sequence search engine with QSCOP to retrieve, for a given query sequence, all structural information currently available. The resulting search engine is reliable in terms of the quality of results obtained, and it is efficient in that results are displayed instantaneously. The hierarchical organization of QSCOP is used to control the redundancy and diversity of the retrieved hits with the benefit that the often cumbersome and difficult interpretation of search results is an intuitive and straightforward exercise. We demonstrate the use of QSCOP-BLAST by example. The server is accessible at http://qscop-blast.services.came.sbg.ac.at/
Nowadays, metagenomic sample analyses are mainly achieved by comparing them with a priori knowledge stored in data banks. While powerful, such approaches do not allow to exploit unknown and/or "unculturable" species, for instance estimated at 99% for Bacteria.
This work introduces Compareads, a de novo comparative metagenomic approach that returns the reads that are similar between two possibly metagenomic datasets generated by High Throughput Sequencers. One originality of this work consists in its ability to deal with huge datasets. The second main contribution presented in this paper is the design of a probabilistic data structure based on Bloom filters enabling to index millions of reads with a limited memory footprint and a controlled error rate.
We show that Compareads enables to retrieve biological information while being able to scale to huge datasets. Its time and memory features make Compareads usable on read sets each composed of more than 100 million Illumina reads in a few hours and consuming 4 GB of memory, and thus usable on today's personal computers.
Using a new data structure, Compareads is a practical solution for comparing de novo huge metagenomic samples. Compareads is released under the CeCILL license and can be freely downloaded from http://alcovna.genouest.org/compareads/.
We analyzed a longitudinal collection of query logs of a full-text search engine designed to facilitate information retrieval in electronic health records (EHR). The collection, 202,905 queries and 35,928 user sessions recorded over a course of 4 years, represents the information-seeking behavior of 533 medical professionals, including frontline practitioners, coding personnel, patient safety officers, and biomedical researchers for patient data stored in EHR systems. In this paper, we present descriptive statistics of the queries, a categorization of information needs manifested through the queries, as well as temporal patterns of the users’ information-seeking behavior. The results suggest that information needs in medical domain are substantially more sophisticated than those that general-purpose web search engines need to accommodate. Therefore, we envision there exists a significant challenge, along with significant opportunities, to provide intelligent query recommendations to facilitate information retrieval in EHR.
It is increasingly difficult for clinicians to keep up-to-date with the rapidly growing biomedical literature. Online evidence retrieval methods are now seen as a core tool to support evidence-based health practice. However, standard search engine technology is not designed to manage the many different types of evidence sources that are available or to handle the very different information needs of various clinical groups, who often work in widely different settings.
The objectives of this paper are (1) to describe the design considerations and system architecture of a wrapper-mediator approach to federate search system design, including the use of knowledge-based, meta-search filters, and (2) to analyze the implications of system design choices on performance measurements.
A trial was performed to evaluate the technical performance of a federated evidence retrieval system, which provided access to eight distinct online resources, including e-journals, PubMed, and electronic guidelines. The Quick Clinical system architecture utilized a universal query language to reformulate queries internally and utilized meta-search filters to optimize search strategies across resources. We recruited 227 family physicians from across Australia who used the system to retrieve evidence in a routine clinical setting over a 4-week period. The total search time for a query was recorded, along with the duration of individual queries sent to different online resources.
Clinicians performed 1662 searches over the trial. The average search duration was 4.9 ± 3.2 s (N = 1662 searches). Mean search duration to the individual sources was between 0.05 s and 4.55 s. Average system time (ie, system overhead) was 0.12 s.
The relatively small system overhead compared to the average time it takes to perform a search for an individual source shows that the system achieves a good trade-off between performance and reliability. Furthermore, despite the additional effort required to incorporate the capabilities of each individual source (to improve the quality of search results), system maintenance requires only a small additional overhead.
Evidence-based medicine; clinical decision support systems; information retrieval; meta-search filters
The biomedical text mining community has focused on developing techniques to automatically extract important relations between biological components and semantic events involving genes or proteins from literature. In this paper, we propose a novel approach for mining relations and events in the biomedical literature using approximate subgraph matching. Extraction of such knowledge is performed by searching for an approximate subgraph isomorphism between key contextual dependencies and input sentence graphs. Our approach significantly increases the chance of retrieving relations or events encoded within complex dependency contexts by introducing error tolerance into the graph matching process, while maintaining the extraction precision at a high level. When evaluated on practical tasks, it achieves a 51.12% F-score in extracting nine types of biological events on the GE task of the BioNLP-ST 2011 and an 84.22% F-score in detecting protein-residue associations. The performance is comparable to the reported systems across these tasks, and thus demonstrates the generalizability of our proposed approach.
Many users search the Internet for answers to health questions. Complementary and alternative medicine (CAM) is a particularly common search topic. Because many CAM therapies do not require a clinician's prescription, false or misleading CAM information may be more dangerous than information about traditional therapies. Many quality criteria have been suggested to filter out potentially harmful online health information. However, assessing the accuracy of CAM information is uniquely challenging since CAM is generally not supported by conventional literature.
The purpose of this study is to determine whether domain-independent technical quality criteria can identify potentially harmful online CAM content.
We analyzed 150 Web sites retrieved from a search for the three most popular herbs: ginseng, ginkgo and St. John's wort and their purported uses on the ten most commonly used search engines. The presence of technical quality criteria as well as potentially harmful statements (commissions) and vital information that should have been mentioned (omissions) was recorded.
Thirty-eight sites (25%) contained statements that could lead to direct physical harm if acted upon. One hundred forty five sites (97%) had omitted information. We found no relationship between technical quality criteria and potentially harmful information.
Current technical quality criteria do not identify potentially harmful CAM information online. Consumers should be warned to use other means of validation or to trust only known sites. Quality criteria that consider the uniqueness of CAM must be developed and validated.
Quality; harm; Internet; medical information; World Wide Web; complementary and alternative medicine
The MEDICOM (Medical Products Electronic Commerce) Portal provides the electronic means for medical-equipment manufacturers to communicate online with their customers while supporting the Purchasing Process and Post Market Surveillance. The Portal offers a powerful Internet-based search tool for finding medical products and manufacturers. Its main advantage is the fast, reliable and up-to-date retrieval of information while eliminating all unrelated content that a general-purpose search engine would retrieve. The Universal Medical Device Nomenclature System (UMDNS) registers all products. The Portal accepts end-user requests and generates a list of results containing text descriptions of devices, UMDNS attribute values, and links to manufacturer Web pages and online catalogues for access to more-detailed information. Device short descriptions are provided by the corresponding manufacturer. The Portal offers technical support for integration of the manufacturers' Web sites with itself. The network of the Portal and the connected manufacturers' sites is called the MEDICOM system.
To establish an environment hosting all the interactions of consumers (health care organizations and professionals) and providers (manufacturers, distributors, and resellers of medical devices).
A representative group of users evaluated the system. The aim of the evaluation was validation of the usability of all of MEDICOM's functionality. The evaluation procedure was based on ISO/IEC 9126 Information technology - Software product evaluation - Quality characteristics and guidelines for their use.
The overall user evaluation of the MEDICOM system was very positive. The MEDICOM system was characterized as an innovative concept that brings significant added value to medical-equipment commerce.
The eventual benefits of the MEDICOM system are (a) establishment of a worldwide-accessible marketplace between manufacturers and health care professionals that provides up-to-date and high-quality product information in an easy and friendly way and (b) enhancement of the efficiency of marketing procedures and after-sales support.
Electronic commerce, medical devices, equipment and supplies, Internet, CORBA, XML, RDBMS