Repository logo
 

An ontology-driven approach for structuring scientific knowledge for predicting treatment adherence behaviour: a case study of tuberculosis in Sub-Saharan African communities.

dc.contributor.advisorMoodley, Deshendran.
dc.contributor.advisorPillay, Anban Woolaganathan.
dc.contributor.advisorSeebregts, Christopher.
dc.contributor.authorOgundele, Olukunle Ayodeji.
dc.date.accessioned2018-10-02T12:55:08Z
dc.date.available2018-10-02T12:55:08Z
dc.date.created2016
dc.date.issued2016
dc.descriptionDoctor of Philosophy in Mathematics, Statistics and Computer Science. University of KwaZulu-Natal, Durban 2016.en_US
dc.description.abstractPoor adherence to prescribed treatment is a complex phenomenon and has been identified as a major contributor to patients developing drug resistance and failing treatment in sub-Saharan African countries. Treatment adherence behaviour is influenced by diverse personal, cultural and socio-economic factors that may vary drastically between communities in different regions. Computer based predictive models can be used to identify individuals and communities at risk of non-adherence and aid in supporting resource allocation and intervention planning in disease control programs. However, constructing effective predictive models is challenging, and requires detailed expert knowledge to identify factors and determine their influence on treatment adherence in specific communities. While many clinical studies and abstract conceptual models exist in the literature, there is no known concrete, unambiguous and comprehensive computer based conceptual model that categorises factors that influence treatment adherence behaviour. The aim of this research was to develop an ontology-driven approach for structuring knowledge of factors that influence treatment adherence behaviour and for constructing adherence risk prediction models for specific communities. Tuberculosis treatment adherence in sub-Saharan Africa was used as a case study to explore and validate the approach. The approach provides guidance for knowledge acquisition, for building a comprehensive conceptual model, its formalisation into an OWL ontology, and generation of probabilistic risk prediction models. The ontology was evaluated for its comprehensiveness and correctness, and its effectiveness for constructing Bayesian decision networks for predicting adherence risk. The approach introduces a novel knowledge acquisition step that guides the capturing of influencing factors from peer-reviewed clinical studies and the scientific literature. Furthermore, the ontology takes an evidence based approach by explicitly relating each factor to published clinical studies, an important consideration for health practitioners. The approach was shown to be effective in constructing a flexible and extendable ontology and automatically generating the structure of a Bayesian decision network, a crucial step towards automated, computer based prediction of adherence risk for individuals in specific communities.en_US
dc.identifier.urihttp://hdl.handle.net/10413/15573
dc.language.isoen_ZAen_US
dc.subjectTheses - Computer Science.en_US
dc.subject.otherTuberculosis.en_US
dc.subject.otherTuberculosis in Africa.en_US
dc.subject.otherOwl Ontology.en_US
dc.subject.otherTB treatment adherence.en_US
dc.titleAn ontology-driven approach for structuring scientific knowledge for predicting treatment adherence behaviour: a case study of tuberculosis in Sub-Saharan African communities.en_US
dc.typeThesisen_US

Files

Original bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
Ogundele_ Olukunle_A_2016.pdf
Size:
2.53 MB
Format:
Adobe Portable Document Format
Description:

License bundle

Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.64 KB
Format:
Item-specific license agreed upon to submission
Description: