Yildirim,P.Ceken,K.Saka,O.2024-10-152024-10-1520112978-145770041-5[SCOPUS-DOI-BELIRLENECEK-148]2-s2.0-83155190293https://hdl.handle.net/20.500.14517/6732Medline articles are rich resources for discovering hidden knowledge for the treatments of liver specific parasites. Knowledge acquisition from these articles requires complex processes depending on biomedical text mining techniques. In this study, name entity recognition and hierarchical clustering techniques were used for advanced drug analyses. Drugs were extracted from the articles belonging to specific time periods and hierarchical clustering was applied on parasite and drug datasets. Hierarchical clustering results revealed that some parasites have similar in terms of treatment and the others are different. Our results also showed that, there have not been major changes in the treatment of liver specific parasites for the past four decades and there are problems associated with the development of new drugs. Both pharmaceutical initiatives and healthcare providers should investigate major drawbacks and develop some strategies to overcome these problems. © 2011 Polish Info Processing Soc.eninfo:eu-repo/semantics/closedAccessBiomedical Text MiningClustering AnalysisLiverParasiteConference Object165168