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Erschienen in:
Patient Flow Monitoring Systems: Investigation of Alternatives Kapitel lesen Erstes Kapitel lesen

2017 | OriginalPaper | Buchkapitel

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

verfasst von : Olukunle A. Ogundele, Deshendran Moodley, Christopher J. Seebregts, Anban W. Pillay

Erschienen in: Software Engineering in Health Care

Verlag: Springer International Publishing

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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.

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Vorheriges Kapitel Reasoning About Confidence and Uncertainty in Assurance Cases: A Survey
Nächstes Kapitel From Requirements to Code: Model Based Development of a Medical Cyber Physical System
Fußnoten
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Metadaten
Titel
Building Semantic Causal Models to Predict Treatment Adherence for Tuberculosis Patients in Sub-Saharan Africa
verfasst von
Olukunle A. Ogundele
Deshendran Moodley
Christopher J. Seebregts
Anban W. Pillay
Copyright-Jahr
2017
Buch
Software Engineering in Health Care

Print ISBN: 978-3-319-63193-6

Electronic ISBN: 978-3-319-63194-3

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-63194-3_6

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