An ontology for factors affecting tuberculosis treatment adherence behavior in sub-Saharan Africa
Authors Ogundele OA, Moodley D, Pillay A, Seebregts C
Received 12 September 2015
Accepted for publication 19 January 2016
Published 27 April 2016 Volume 2016:10 Pages 669—681
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 4
Editor who approved publication: Dr Johnny Chen
Olukunle Ayodeji Ogundele,1 Deshendran Moodley,1 Anban W Pillay,1 Christopher J Seebregts1,2
1UKZN/CSIR Meraka Centre for Artificial Intelligence Research and Health Architecture Laboratory, School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Durban, KwaZulu-Natal, 2Jembi Health Systems NPC, Cape Town, South Africa
Purpose: Adherence behavior is a complex phenomenon influenced by diverse personal, cultural, and socioeconomic factors that may vary between communities in different regions. Understanding the factors that influence adherence behavior is essential in predicting which individuals and communities are at risk of nonadherence. This is necessary for supporting resource allocation and intervention planning in disease control programs. Currently, there is no known concrete and unambiguous computational representation of factors that influence tuberculosis (TB) treatment adherence behavior that is useful for prediction. This study developed a computer-based conceptual model for capturing and structuring knowledge about the factors that influence TB treatment adherence behavior in sub-Saharan Africa (SSA).
Methods: An extensive review of existing categorization systems in the literature was used to develop a conceptual model that captured scientific knowledge about TB adherence behavior in SSA. The model was formalized as an ontology using the web ontology language. The ontology was then evaluated for its comprehensiveness and applicability in building predictive models.
Conclusion: The outcome of the study is a novel ontology-based approach for curating and structuring scientific knowledge of adherence behavior in patients with TB in SSA. The ontology takes an evidence-based approach by explicitly linking factors to published clinical studies. Factors are structured around five dimensions: factor type, type of effect, regional variation, cross-dependencies between factors, and treatment phase. The ontology is flexible and extendable and provides new insights into the nature of and interrelationship between factors that influence TB adherence.
Keywords: tuberculosis, treatment adherence behavior, influencing factor, conceptual model, ontology
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