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

Ogundele, OA and Moodley, D and Seebregts, CJ and Pillay, AW (2017) Building Semantic Causal Models to Predict Treatment Adherence for Tuberculosis Patients in Sub-Saharan Africa, Proceedings of 4th International Symposium on Foundations of Health Information Engineering and Systems, FHIES 2014 and 6th International Workshop on Software Engineering in Health Care, SEHC 2014, Lecture Notes in Computer Science, 17 July 2014 through 18 July 2014, Washington; United States;, 9062, 81-95, Springer Verlag.

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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.

Item Type:	Conference paper
Subjects:	Computing methodologies > Artificial intelligence
Alternate Locations:	https://link.springer.com/chapter/10.1007%2F978-3-319-63194-3_6
Date Deposited:	23 Nov 2017
Last Modified:	10 Oct 2019 15:31
URI:	http://pubs.cs.uct.ac.za/id/eprint/1221

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