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

2017

  • Authors:
    Olukunle Ogundele , Deshen Moodley , Chris Seebregts , Anban Pillay

    Publication date:
    2017

    Institution:
    CSIR Meraka Institute, UKZN, UCT

    Output type:
    Conference proceedings

    Springer

    Abstract:

    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