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Author: search.filters.author.Leerojanaprapa, K.Has files: No

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    Item type:Publication,
    Applying Bayesian network for noncommunicable diseases risk analysis: Implementing national health examination survey in Thailand
    (2017-07-02)
    Leerojanaprapa, K.
    ;
    Atthirawong, W.
    ;
    Aekplakorn, W.
    ;
    Sirikasemsuk, K.
    We propose using a Bayesian network to capture and understand the dependency risk factors affecting the prevalence of chronic diseases. By applying a Bayesian network model, we can visualize interdependencies between risks and their effects on the Noncommunicable disease (NCD) prevalence. By using a Bayesian network to model the prevalence of diabetes, we can define the top three risks as family history of diabetes, obesity, and age. Furthermore, the risk classification results can help to determine the managing strategy. For the Thai population, problems arising from family history of diabetes and obesity can be met by employing a transfer strategy. Age (especially ages of 35-59) and the risk incurred by low intake of fruits and vegetables should use a reduction or mitigation strategy. Finally, those at risk as a result of their area of residence (in urban areas) and socio-economic factors within the 4<sup>th</sup> quantile and low level of physical activity should apply a retain strategy.
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    Modeling supply risk using belief networks: A process with application to the distribution of medicine
    (2014-11-18)
    Leerojanaprapa, K.
    ;
    Van Der Meer, R.
    ;
    Walls, L.
    We propose a modeling approach based on belief networks to capture and understand the systemic nature of risks affecting supply networks. By aligning the purpose of a model with the nature of supply management decisions, we provide a mechanism for identifying relevant supply risks so that we can visualize inter-dependencies between risks and predict their effects on supply performance. By using a belief network modeling formalism we can use diagnostics to understand the key drivers of unwanted risk scenarios and to explore the efficacy of possible risk mitigating actions. We illustrate how belief network modeling can be used to manage the risk/reward position and provide new insights into supply risks through an example for the medicine supply chain of a regional health service provider.