Buurman, H

Diwersy, M

Mons, B

Schmidt, M

van Mulligen, Erik

Undetermined

EUR Research Repository

EUR Research Information PortalFacilitating networks of informationPublished in:Journal of the American Medical Informatics AssociationPublication status and date:Published: 01/01/2000Document VersionPublisher's PDF, also known as Version of recordCitation for the published version (APA):van Mulligen, E., Diwersy, M., Schmidt, M., Buurman, H., & Mons, B. (2000). Facilitating networks of information. Journal ofthe American Medical Informatics Association, 7, 868-872. http://hdl.handle.net/1765/9537Link to publication on the EUR Research Information PortalTerms and Conditions of UseExcept as permitted by the applicable copyright law, you may not reproduce or make this material available to any third partywithout the prior written permission from the copyright holder(s). 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In case of a legitimate complaint,we will make the material inaccessible and/or remove it from the website.Facilitating Networks of InformationErik M. van Mulligen1, Mario Diwersy2, Martin Schmidt2, Henk Buurman3, Barend Mons41Dept. of Medical Informatics, Erasmus University Rotterdam, The Netherlands2SyynX WebSolutions GmbH, Frankfurt, Germany3Collexis, The Netherlands4Netherlands Organization for Scientific Research, The Hague, The NetherlandsIn this paper we describe an approach to respond toa request for information with the identification andlocation of the appropriate person as a source ofinformation for answering the question. The expertiseof a person is characterized using a weighted profilethat has been derived from a series of documentsdescribing the expert’s activities. Having theseprofiles, requests for information can be matchedwith these profiles. The best matches correspond withthe people that are experts for providing informationon the request.BackgroundThe information society is flooding us withinformation. Not only the web is drowning us withover a billion pages of information [1], the number ofbiomedical journals also doubles on the averageevery 19 years to a total number of about 10.000 in1980 [2]. Since (web) search engines are yieldingtons of web pages for non-specific queries, users arerequired to become more specific and, as aconsequence, loose sensitivity (i.e., missing pieces ofinformation). In addition, the major search enginesare only covering at best 34% of all pages, confinedas they are in bandwidth and  processing powermaintaining those large word indexes [3]. There is aclear need for effective information retrieval tools.Clinical Information Seeking StrategiesWith the need for ubiquitous information access,various studies have been performed to analyze howclinicians will find their information. Comparing thereported use of information sources with the observeduse showed a discrepancy. Physicians reported to useprint resources in 61% and human resources in 32%of the cases. However, the observation showed adifferent picture: in 53% of the questions humanresources were consulted and only in 27% printresources [4]. Unavailability of print resources andthe confined time during patient visits to seek forinformation are the most important reasons whyhuman resources are playing an important role [5,6].Digital Access to InformationMany projects have been conducted to facilitate andoptimize the routine searching and retrieval ofinformation. These projects can be roughlycategorized in building information directories,improving the information retrieval facilities, endingup with knowledge management tools.Information DirectoriesThe most common step in improving access toinformation is maintaining directories classifying theinformation by subject. A user browsing the web canadd a reference to an interesting web page to a localdirectory of references. A clear disadvantage of thisapproach is the fact that only after discovery of theinformation a bookmark can be set. Dynamic pages(i.e., generated from information in a database) cannot be marked and static pages can be moved ordeleted, causing broken links.To support groups of users, the bookmark directorycan also be maintained at a server side: the internetportals [7,8]. In this approach, references toinformation are collected for a common group ofusers and organized by the provider of the internetportal. Commercial interests may influence theorganization and the contents of the portal and thequestion is whether the aim of the portal is consistentwith the user’s interests.User-driven Information retrievalAnother approach is the use of search engines to findinformation that is interesting. Various searchengines have been developed [9,10]. The underlyingassumption in search engines is that phrasing a queryusing a number of words will yield the page(s)containing relevant information. Since most websearch engines are generic search engines, (domainspecific) synonyms and homonyms will not be dealtwith in the search. For routine use, the informationretrieved by the generic search engines is not goodenough. Typically, a search will yield thousands ofhits. However, adding search criteria in order toreduce the number of hits will in general diminish thesensitivity of the query. As a consequence, severalprojects focus on the development of a preprocessorthat will extend the generic search engines withdomain knowledge [11,12,13] and thus improve thesensitivity and specificity of the query.Another approach to improve information retrieval isthe active channeling approach. In this approach, onecan define subjects of interest and the software willsearch in the background for information concerningthe subjects. The advantage of this approach is thatthe querying is on the background and is not wastingthe user’s (web surfing) time.System-driven Information ManagementThe approaches discussed above all have atransaction-oriented approach: a query is formulatedand the results are fed back. There is hardly anyinteraction during the search that requires the user torefine and make choices during the informationretrieval phase. Agent technology focuses on anapproach that at certain moments interacts with theuser to refine and optimize its search for information[14,15]. This approach is still a research topic: agentprogramming languages [16] and agent - userinteraction are still research subjects.ShortcomingsDespite these efforts and developments ininformation management, these approaches are stillnot used for daily practice. Typically, entering a non-specific request will yield a large amount of webpages with information that can not be managedquickly, whereas a more specific request will lead tomissing the relevant information.Approach in AWAREThe best source for information remains the humanexpert. All studies on the information seekingbehavior of clinicians show that the best and firstsource for finding information is the clinical expert orcolleague [4,5,6]. The effort to seek informationusing an expert is only a fraction of searching digitalor print information sources. Not only clinicians, butalso people in health research, publishing, companies,etc. are seeking for experts that can be consulted toprovide answers on a particular request forinformation. Each of these is confronted with the factthat effective – effective meaning only a small set ofrelevant pages - information retrieval is still notavailable.In the AWARE (Automated Web-based ArchiveRetrieval and Exchange) system, the answer to arequest for information is the name of the sourcewhere the information can be found. In the firstversion of the system, the electronic business cards ofexperts are shown to the user as a result of a query. Inthe future, this can be extended to also include onlineinformation sources and online databases.Structure of AWAREThe AWARE system consists of a web-based databasewith three major sections: institutions, people, andactivities. The institutions section gives informationon various institutes active in the application field(e.g., for health research in developing countries theinstitutions conduct research projects in thosecountries). The institution section links to the peoplesection to indicate what people work in each institute.The people section contains the electronic businesscards of people. The people section is linked with theactivity section, specifying what activities the user iscarrying out. These activities can contain projectdescriptions, curriculum vitae, scientific publications.In fact, anything that is related to the person’sexpertise can be entered here. Apart from matching arequest for information with the most relevantelectronic business cards, the user can also browsethe database and follow the links between the varioussections (i.e., traverse from organization to the peopleto the activities and vice versa) .Figure 1. Overview of the different sections of thedatabase. The Organizations, People, and Activitiessections are linked and can be browsed, yieldingdynamic combinations of these three sections.Profile informationIn order to be able to match a request for informationwith the information in the database, a categorizationor indexing tool has been used. Attached to eachperson’s business card is a list of activities. The textof all these activities is indexed. In this indexing step,the concepts from the Unified Medical LanguageSystem (UMLS) [17] that best fit the text are attachedto the text including a weight factor. This index canbe interactively adjusted by the user. All profilesfrom the texts attached to a person’s business card arecombined in a single profile for the user. All profilesof all people working for an institution are combinedin a same way to generate the profile of an institution.In the running version of AWARE, a request forinformation can only include a single concept fromUMLS. In a next version of the software, the requestfor information can be entered as a description. Thedescriptive request will also be indexed and theresulting request profile will be matched against theprofiles in the database.Indexing softwareThe indexing software combines a statistical indexingalgorithm with a rich thesaurus. This thesaurus hasbeen derived from the contents of the UMLS andconsists of three parts: concepts, terms (denotationsfor the concept from the various coding systems), andnormalized or stemmed words derived from thewords in the terms. The reference from normalizedwords to concepts is used in the indexing algorithmto find a list of candidate UMLS concepts. The wordsfrom the text are clustered: words within a certaindistance that refer to the same concept are combined.After the clustering, statistical features are computedfor each of the concepts. Finally, on basis of thesefeatures the most probable concepts are selected andshown to the user.The following statistical features are computed:Per concept:§ Specificity. The specificity is indicated bythe number of concepts that are referred by anormalized word in the text. The specificityof a cluster is defined as the minimalspecificity for all individual words containedin the cluster. A concept is more probable ifit contains a word that is only rarely used inthe vocabulary and the text contains thatparticular word.§ Similarity. The textual similarity featureindicates what fraction of the concept nameis contained in the text. A concept is moreprobable if a large part of its term is coveredby words from the text.§ Co-occurrence. The co-occurrence measureindicates how often a concept co-occurs(based on MedLine and other knowledgesources) with other concepts found in thedocument. If two candidate concepts areoften used in combination, their probabilitieswill rise.Per cluster:§ Dispersion. The dispersion is calculated percluster. It is the mean distance in wordsbetween words referring to one conceptFigure 2.  Overview of the AWARE system showing a project text andthe weighted index as computed by the indexing software. The terms can(since a cluster contains concepts withalmost identical words, this measure iscalculated per cluster). If there is only oneword referring to the concept, the distance isset to the number of words in the document(this negatively discriminates concepts thatare only referred by one word). Therationale behind dispersion is that a conceptis more likely to be present if the wordsreferring to it are close to each other.§ Cluster Size. The cluster size is the numberof concepts/alternatives in a cluster. Aconcept is less probable if there are manyalternative concepts in the cluster.Per document:§ Frequency. The frequency is the number ofwords or clusters referring to one concept. Aconcept is more probable if the text containsmultiple references to that concept.Add-to-AWARE buttonIn order to facilitate the uploading of documents ortexts to the AWARE system, a button has been definedthat can be included on the menu-bar of MicrosoftInternet Explorer. With this button, the text containedin the Internet browser will be sent to the AWAREweb-server, indexed, and the text profile will beadded to the user’s personal profile. Since this buttonis available in the user’s desktop environment, theeffort to maintain one’s profile is minimal. Figure 3shows the web-page that lists the documents thathave been uploaded to the AWARE system. The termsthat have been assigned by the indexing software canbe manually reviewed and, if necessary, adjusted. Ifthe terms are adjusted, the profile is automaticallyupdated.ImplementationThis Aware system has been implemented in anapplication for health researchers in developingcountries. With this system, an information networkof experts in health research in developing countrieshas been created that can be contacted for specificinformation. The profiles in this Aware-based systemSHARED (Scientists for Health And REsearchDevelopment, www.shared.de) are based on healthresearch project descriptions in developing countries.The system has been developed as a light-weightsystem, giving people in developing countries thepossibility to quickly access the web-based databaseand locating the most appropriate source ofinformation.EvaluationThe indexing software has been evaluated using acollection of about 1000 MedLine abstracts with theirmanually assigned MeSH headings. The indexingsoftware is able to correctly identify 85% of theMeSH headings. Baring in mind the fact that themanual assignment of MeSH headings is based in thefull article text rather than on the abstract and the factthat the indexing software has been limited to onlyMeSH headings rather than the whole UMLS, theresults are quite well. A paper describing these resultshas been submitted for publication. A formalevaluation of the complete system has not yet beendone, but will be started soon.DiscussionInformation delivery is becoming more and moreimportant. However, the current electronic facilitiesare still not effective enough to be used for dailypractice. Therefore, we have focused on informationmediation, i.e., on identifying the most promisingsource for finding that information. Our firstapproach is aiming at developing a network ofexperts that can be consulted for requests ofinformation. This concept has been implemented forhealth research and development in developingcountries. However, these networks of informationsources can also be beneficial for health care ingure 3. This window shows the documents thatve been uploaded with the "add-to-aware"tton and the resulting personal profile.developed countries, for scientists who want to findother experts in the field, etc.The availability of the add-to-aware button on theuser’s desktop minimizes the burden of maintainingthe personal profile in the database. We are currentlyexploring ways in health science to create a startingpersonal profile from on-line available scientificpublications.For the near future, we will further improve theindexing software and improve the web-baseddatabase access. We also extend the informationnetwork with the location of print (web-based)information sources.References1 Keslik J,  Kleine S. Web Surpasses One BillionDocuments.http://www.inktomi.com/new/press/billion.html2 Wyatt J. Use and sources of medical knowledge.The Lancet 1991;338:1368-73.3 Lawrence S, Giles CL. Searching the WorldWide Web. Science 1998;280(5360):98-100.4 Covell DG, Gwen C, Uman RN, Manning PhR.Information Needs in Office Practice: Are TheyBeing Met? Annals of Internal Medicine1985;103:596-9.5 Smith R. What Clinical information do doctorsneed? BMJ 1996;313:1062-86 Hall EF. Physical Therapists in Private Practice:Information Sources and Information Needs. BullMed Libr Assoc 1995;83(2):196-201.7 Garofalakis MN, Ramaswamy S, Rastogi R,Shim K. Of Crawlers, Portals, Mice, and Men: Isthere more to Mining the Web? Proceedings ofACM SIGMOD'99, Philadelphia, Pennsylvania,1999:504.8 Hersh WR, Brown KE, Donohoe LC, et al.CliniWeb: Managing Clinical Information on theWorld Wide Web. J Am Med Inform Assoc.1996;3:273-80.9 Giles CL, Lawrence S. Accessibility ofinformation on the web. Nature 1999;400:107-109.10 Lawrence S, Giles CL. Searching the WorldWide Web. Science 1998;280:98-100.11 Joubert M, Fieschi M, Robert JJ, Volot F, FieschiD. UMLS-based Conceptual Queries toBiomedical Information Databases: An Overviewof the Project ARIANE. J Am Med Inform Assoc1998;5(1):52-61.12 Cousins SB, Silverstein JC, Fris se ME. QueryNetworks for Medical Information Retrieval –Assigning Probabilistic Relationships. In: MillerRA, ed. Proceedings of the Fourteenth AnnualSymposium on Computer Applications inMedical Care. Los Alamitos CA: IEEE ComputerSociety Press. 1990:800-4.13 Detmer WM, Barnett GO, Hersh WR.MedWeaver: integrating decision support,literature searching, and Web exploration usingthe UMLS MetaThesaurus. In: Proceedings ofthe AMIA Annual  Fall Symposium. 1997:490-4.14 Genesereth MR, Ketchpel SP. Soft ware Agents.Comm ACM 1994;37(7):48-53.15 Bollacker KD, Lawrence S, Giles CL. CiteSeer:An Autonomous Web Agent for AutomaticRetrieval and Identification of InterestingPublications. Proceedings of the 2nd InternationalConference on Autonomous Agents. ACM Press,New York, 1998:116-23.16 Mendelzon AO, Mihaila GA, Milo T. Queryingthe World Wide Web. Int J Dig Libr 1997;1(1):54-67.17 Lindberg DAB, Humphreys BL, McCray AT.The unified medical language system. Meth InfMed, 32:281-91, 1993.Address for correspondence:Erik M. van MulligenDept. of Medical Informatics,Erasmus University Rotterdam,P.O. Box 1738, 3000 DR, RotterdamThe Netherlandsvanmulligen@mi.fgg.eur.nl

Facilitating networks of information

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Facilitating networks of information

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