Autism research dynamic through ontology-based text mining
Author:
Macedoni Luksic Marta,Urbancic Tanja,Petric Ingrid,Cestnik Bojan
Abstract
Purpose
– The increase of prevalence of autism spectrum disorders (ASD) has been accompanied by much new research. The amount and the speed of growth of scientific information available online have strongly influenced the way of work in the research community which calls for new methods and tools to support it. The purpose of this paper is to present ontology-based text mining in the field of autism trend analysis that may help to understand the broader picture of the disorder since its discovery.
Design/methodology/approach
– The data sets consisted of abstracts of more than 18,000 articles on ASD published from 1943 to the end of 2012 found in MEDLINE and of the documents’ titles for all those articles where the abstracts were not available.
Findings
– In this way, the authors demonstrated a steeper exponential curve of ASD publications compared with all publications in MEDLINE. In addition, the main research topics over time were identified using the “open discovery” approach. Finally, the relationship between a priori setting up research topics including communication, genetics, environmental risk factors, vaccination and adulthood were revealed.
Originality/value
– Using ontology-based text mining the authors were able to identify the main research topics in the field of autism during the time, as well as to show the dynamics of some research topics as a priori setting up. The computerised methodology that was used allowed the authors to analyse a much larger quantity of information, saving time and manual work.
Subject
Psychiatry and Mental health,Cognitive Neuroscience,Clinical Neurology,Neurology,Developmental and Educational Psychology
Reference26 articles.
1. American Psychiatric Association
(2013),
Diagnostic and Statistical Manual of Mental Disorders
, 5th ed., American Psychiatric Publication, Arlington, VA. 2. Brank, J.
,
Grobelnik, M.
,
Milič-Frayling, N.
and
Mladenić, D.
(2002), “Feature selection using support vector machines”, Proceedings of the 3rd International Conference on Data Mining Methods and Databases for Engineering, Finance, and Other Fields, Bologna. 3. Bruza, P.
and
Weeber, M.
(2008),
Literature-Based Discovery
, Springer, Berlin. 4. Cestnik, B.
,
Petrič, I.
,
Urbančič, T.
and
Macedoni-Lukšič, M.
(2007), “Structuring domain knowledge by semi-automatic ontology construction”,
Organizacija
, Vol. 40 No. 6, pp. 233-8. 5. Cohen, A.M.
and
Hersh, W.R.
(2005), “A survey of current work in biomedical text mining”,
Brief Bioinformatics
, Vol. 6 No. 1, pp. 57-71.
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