Thinking PubMed: an Innovative System for Mental Health Domain

Information regarding mental illness is dispersed over various resources but even within a specific resource, such as PubMed, it is difficult to link this information, to share it and find specific information when needed. Specific and targeted searches are very difficult with current search engines as they look for the specific string of letters within the text rather than its meaning. In this paper we present thinking PubMed as a system that results from synergy of ontology and data mining technologies and performs intelligent information searches using the domain ontology. Furthermore, the thinking PubMed analyzes and links the retrieved information, and extracts hidden patterns and knowledge using data mining algorithms. This is a new generation of information-seeking tool where the ontology and data-mining work in concert to increase the value of the available information.

[1]  Tharam S. Dillon,et al.  Mining of Health Information from Ontologies , 2008, HEALTHINF.

[2]  Michael Schroeder,et al.  GoPubMed: exploring PubMed with the Gene Ontology , 2005, Nucleic Acids Res..

[3]  Sergei Egorov,et al.  MedScan, a natural language processing engine for MEDLINE abstracts , 2003, Bioinform..

[4]  Asunción Gómez-Pérez,et al.  Towards a framework to verify knowledge sharing technology , 1996 .

[5]  Olivier Bodenreider,et al.  The Unified Medical Language System (UMLS): integrating biomedical terminology , 2004, Nucleic Acids Res..

[6]  Jun'ichi Tsujii,et al.  GENIA corpus - a semantically annotated corpus for bio-textmining , 2003, ISMB.

[7]  Sougata Mukherjea,et al.  Enhancing a biomedical information extraction system with dictionary mining and context disambiguation , 2004, IBM J. Res. Dev..

[8]  Tharam S. Dillon,et al.  Automated knowledge acquisition , 1994, Prentice Hall International series in computer science and engineering.

[9]  Hans-Michael Müller,et al.  Textpresso: An Ontology-Based Information Retrieval and Extraction System for Biological Literature , 2004, PLoS biology.

[10]  Sarah Lewis,et al.  Genetic epidemiology and public health: hope, hype, and future prospects , 2005, The Lancet.

[11]  H. S. Pinto.  Knowledge Sharing and Reuse , 2022 .

[12]  I. F. Chang,et al.  Searching for information on the Internet using the UMLS and Medical World Search , 1997, AMIA.

[13]  R Brian Haynes,et al.  Optimal search strategies for identifying mental health content in MEDLINE: an analytic survey , 2006, Annals of general psychiatry.

[14]  Tharam S. Dillon,et al.  Tree Mining in Mental Health Domain , 2008, Proceedings of the 41st Annual Hawaii International Conference on System Sciences (HICSS 2008).

[15]  Fedja Hadzic,et al.  Implications of frequent subtree mining using hybrid support definitions , 2007 .

[16]  Tharam S. Dillon,et al.  UNI3 - efficient algorithm for mining unordered induced subtrees using TMG candidate generation , 2007, 2007 IEEE Symposium on Computational Intelligence and Data Mining.

[17]  Gregory Piatetsky-Shapiro,et al.  Microarray data mining: facing the challenges , 2003, SKDD.

[18]  Jong C. Park,et al.  Bioie: Retargetable Information Extraction and Ontological Annotation of Biological Interactions from the Literature , 2004, J. Bioinform. Comput. Biol..

[19]  A. Hansell,et al.  Genetic epidemiology and public health: hope, hype, and future prospects , 2005, The Lancet.

[20]  Uwe Reyle,et al.  Ontology-driven discourse analysis for information extraction , 2005, Data Knowl. Eng..