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.
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Acknowledgement
This work, including support for the Health Architecture Laboratory (HeAL) project as well as for DM, CS and AP and a PhD scholarship to KO, was funded by grants from the Rockefeller Foundation (Establishing a Health Enterprise Architecture Lab, a research laboratory focused on the application of enterprise architecture and health informatics to resource-limited settings, Grant Number: 2010 THS 347) and the International Development Research Centre (IDRC) (Health Enterprise Architecture Laboratory (HeAL), Grant Number: 106452-001). CS was additionally funded for aspects of this work by a grant from the Delegation of the European Union to South Africa to the South African Medical Research Council (SANTE 2007 147-790; Drug resistance surveillance and treatment monitoring network for the public sector HIV antiretroviral treatment programme in the Free State). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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Ogundele, O.A., Moodley, D., Seebregts, C.J., Pillay, A.W. (2017). Building Semantic Causal Models to Predict Treatment Adherence for Tuberculosis Patients in Sub-Saharan Africa. In: Huhn, M., Williams, L. (eds) Software Engineering in Health Care. SEHC FHIES 2014 2014. Lecture Notes in Computer Science(), vol 9062. Springer, Cham. https://doi.org/10.1007/978-3-319-63194-3_6
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DOI: https://doi.org/10.1007/978-3-319-63194-3_6
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