Building Semantic Causal Models to Predict Treatment Adherence for Tuberculosis Patients in Sub-Saharan Africa
Ogundele, OA, D Moodley, CJ Seebregts and AW Pillay (2017) Building Semantic Causal Models to Predict Treatment Adherence for Tuberculosis Patients in Sub-Saharan Africa. In Proceedings 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 9062, pages 81-95, Washington; United States;.
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.
|EPrint Type:||Conference Paper|
|Subjects:||I Computing Methodologies: I.2 ARTIFICIAL INTELLIGENCE|
|Deposited By:||Moodley, Deshen|
|Deposited On:||23 November 2017|