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2023
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Explaining socio-demographic and behavioral patterns of vaccination against the swine flu (H1N1) pandemic
Punzi C., Maslennikova A., Gezici G., Pellungrini R., Giannotti F.
Pandemic vaccination campaigns must account for vaccine skepticism as an obstacle to overcome. Using machine learning to identify behavioral and psychological patterns in public survey datasets can provide valuable insights and inform vaccination campaigns based on empirical evidence. However, we argue that the adoption of local and global explanation methodologies can provide additional support to health practitioners by suggesting personalized communication strategies and revealing potential demographic, social, or structural barriers to vaccination requiring systemic changes. In this paper, we first implement a chain classification model for the adoption of the vaccine during the H1N1 influenza outbreak taking seasonal vaccination information into account, and then compare it with a binary classifier for vaccination to better understand the overall patterns in the data. Following that, we derive and compare global explanations using post-hoc methodologies and interpretable-by-design models. Our findings indicate that socio-demographic factors play a distinct role in the H1N1 vaccination as compared to the general vaccination. Nevertheless, medical recommendation and health insurance remain significant factors for both vaccinations. Then, we concentrated on the subpopulation of individuals who did not receive an H1N1 vaccination despite being at risk of developing severe symptoms. In an effort to assist practitioners in providing effective recommendations to patients, we present rules and counterfactuals for the selected instances based on local explanations. Finally, we raise concerns regarding gender and racial disparities in healthcare access by analysing the interaction effects of sensitive attributes on the model's output.Source: xAI 2023 - World Conference on Explainable Artificial Intelligence, pp. 621–635, Lisbon, Portugal, 26-28/07/2023
DOI: 10.1007/978-3-031-44067-0_31
Project(s): HumanE-AI-Net
, SoBigData-PlusPlus
Metrics:
Punzi C., Maslennikova A., Gezici G., Pellungrini R., Giannotti F.
Pandemic vaccination campaigns must account for vaccine skepticism as an obstacle to overcome. Using machine learning to identify behavioral and psychological patterns in public survey datasets can provide valuable insights and inform vaccination campaigns based on empirical evidence. However, we argue that the adoption of local and global explanation methodologies can provide additional support to health practitioners by suggesting personalized communication strategies and revealing potential demographic, social, or structural barriers to vaccination requiring systemic changes. In this paper, we first implement a chain classification model for the adoption of the vaccine during the H1N1 influenza outbreak taking seasonal vaccination information into account, and then compare it with a binary classifier for vaccination to better understand the overall patterns in the data. Following that, we derive and compare global explanations using post-hoc methodologies and interpretable-by-design models. Our findings indicate that socio-demographic factors play a distinct role in the H1N1 vaccination as compared to the general vaccination. Nevertheless, medical recommendation and health insurance remain significant factors for both vaccinations. Then, we concentrated on the subpopulation of individuals who did not receive an H1N1 vaccination despite being at risk of developing severe symptoms. In an effort to assist practitioners in providing effective recommendations to patients, we present rules and counterfactuals for the selected instances based on local explanations. Finally, we raise concerns regarding gender and racial disparities in healthcare access by analysing the interaction effects of sensitive attributes on the model's output.Source: xAI 2023 - World Conference on Explainable Artificial Intelligence, pp. 621–635, Lisbon, Portugal, 26-28/07/2023
DOI: 10.1007/978-3-031-44067-0_31
Project(s): HumanE-AI-Net
, SoBigData-PlusPlus Metrics:
See at:
doi.org 
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