Building Semantic Causal Models to Predict Treatment Adherence for Tuberculosis Patients in Sub-Saharan Africa

  • Olukunle A. Ogundele, Deshendran Moodley, Christopher J. Seebregts, Anban W. Pillay
  • January 2017, Springer Science + Business Media
  • DOI: 10.1007/978-3-319-63194-3_6

Building semantic causal models to predict treatment adherence for TB patients

What is it about?

Poor adherence to prescribed treatment is a major factor contributing to tuberculosis patients developing drug resistance and failing treatment. Treatment adherence behaviour is influenced by diverse personal, cultural and socio-economic factors that vary between regions and communities. Decision network models can potentially be used to predict treatment adherence behaviour. However, determining the network structure (identifying the factors and their causal relations) and the conditional probabilities is a challenging task. To resolve the former we developed an ontology supported by current scientific literature to categorise and clarify the similarity and granularity of factors.

Why is it important?

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. The ontology will support an open, sharable and reusable treatment adherence behaviour knowledge repository to enhance evidence-based decision making for tuberculosis management. The ontology supports construction of a Bayesian decision network model for particular tuberculosis communities.

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The following have contributed to this page: Dr Olukunle A Ogundele