Disease-disease relationships for rheumatic diseases Web-based biomedical textmining and knowledge discovery to assist medical decision making

dc.authoridHolzinger, Andreas/0000-0002-6786-5194
dc.authorscopusid23396282000
dc.authorscopusid36667404500
dc.authorscopusid6505872114
dc.authorwosidYILDIRIM, PINAR/X-1182-2019
dc.authorwosidHolzinger, Andreas/E-9530-2010
dc.contributor.authorHolzinger, Andreas
dc.contributor.authorSimonic, Klaus-Martin
dc.contributor.authorYildirim, Pinar
dc.contributor.otherBilgisayar Mühendisliği / Computer Engineering
dc.date.accessioned2024-05-25T11:21:45Z
dc.date.available2024-05-25T11:21:45Z
dc.date.issued2012
dc.departmentOkan Universityen_US
dc.department-temp[Holzinger, Andreas; Simonic, Klaus-Martin] Med Univ Graz, Inst Med Informat Stat & Documentat, Graz, Austria; [Yildirim, Pinar] Okan Univ, Fac Engn & Architecture, Dept Comp Engn, Istanbul, Turkeyen_US
dc.descriptionHolzinger, Andreas/0000-0002-6786-5194en_US
dc.description.abstractThe MEDLINE database (Medical Literature Analysis and Retrieval System Online) contains an enormously increasing volume of biomedical articles. There is urgent need for techniques which enable the discovery, the extraction, the integration and the use of hidden knowledge in those articles. Text mining aims at developing technologies to help cope with the interpretation of these large volumes of publications. Co-occurrence analysis is a technique applied in text mining and the methodologies and statistical models are used to evaluate the significance of the relationship between entities such as disease names, drug names, and keywords in titles, abstracts or even entire publications. In this paper we present a method and an evaluation on knowledge discovery of disease-disease relationships for rheumatic diseases. This has huge medical relevance, since rheumatic diseases affect hundreds of millions of people worldwide and lead to substantial loss of functioning and mobility. In this study, we interviewed medical experts and searched the ACR (American College of Rheumatology) web site in order to select the most observed rheumatic diseases to explore disease-disease relationships. We used a web based text-mining tool to find disease names and their co-occurrence frequencies in MEDLINE articles for each disease. After finding disease names and frequencies, we normalized the names by interviewing medical experts and by utilizing biomedical resources. Frequencies are normally a good indicator of the relevance of a concept but they tend to overestimate the importance of common concepts. We also used Pointwise Mutual Information (PMI) measure to discover the strength of a relationship. PMI provides an indication of how more often the query and concept co-occur than expected by change. After finding PMI values for each disease, we ranked these values and frequencies together. The results reveal hidden knowledge in articles regarding rheumatic diseases indexed by MEDLINE, thereby exposing relationships that can provide important additional information for medical experts and researchers for medical decision-making.en_US
dc.identifier.citation11
dc.identifier.doi10.1109/COMPSAC.2012.77
dc.identifier.endpage580en_US
dc.identifier.isbn9780769547367
dc.identifier.issn0730-3157
dc.identifier.scopus2-s2.0-84870324592
dc.identifier.scopusqualityQ4
dc.identifier.startpage573en_US
dc.identifier.urihttps://doi.org/10.1109/COMPSAC.2012.77
dc.identifier.urihttps://hdl.handle.net/20.500.14517/620
dc.identifier.wosWOS:000312376000097
dc.identifier.wosqualityN/A
dc.institutionauthorYıldırım, Pınar
dc.language.isoenen_US
dc.publisherIeeeen_US
dc.relation.ispartof36th Annual IEEE International Computer Software and Applications Conference (COMPSAC) -- JUL 16-20, 2012 -- Izmir Inst Technol (IZTECH), Izmir, TURKEYen_US
dc.relation.ispartofseriesProceedings International Computer Software and Applications Conference
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBiomedical text miningen_US
dc.subjectrheumatic diseasesen_US
dc.subjectdisease-disease relationshipsen_US
dc.subjectco-occurrence analysisen_US
dc.subjectPointwise Mutual Information (PMI)en_US
dc.titleDisease-disease relationships for rheumatic diseases Web-based biomedical textmining and knowledge discovery to assist medical decision makingen_US
dc.typeConference Objecten_US
dspace.entity.typePublication
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