Visual Aids For Interpreting Predictive Probability Distributions Obtained From Bayesian Network Models

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2023-1-16

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Alptekin Gökbender, Cansu

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Communicating uncertainty is a challenging task. Personal differences such as culture, cognitive load and even feelings of the user can impact the interpretation of an uncertain situation. Decision support models such as Bayesian networks can aid dealing with uncertainty. However, the outputs of these models are probability distributions, their interpretation can be challenging and visualisations can help with this task. The aim of the study is to investigate how effective visual aids communicating BN predictions are and users preferences regarding these visual aids. Model's prediction and performance are needed to communicate BN predictions. Hence, in this study both model's prediction and performance are investigated. BN model that has been developed to make predictions on a medical condition namely, Trauma Induced Coagulopathy (TIC BN) are used as a case study (Perkins et al., 2020). Our results show that participants' risk understanding and their model performance evaluations are affected by the visual aid used at different risk levels. Results also reveal that while participants' visual aid preferences for model's prediction did not differ, the most preferred visual aid for model's performance is icon array.

Subject Keywords

Risk assessment, Visual aid, Bayesian Network Model, Model performance, Trauma

URI

https://hdl.handle.net/11511/102033

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Graduate School of Informatics, Thesis

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Citation Formats

C. Alptekin Gökbender, “Visual Aids For Interpreting Predictive Probability Distributions Obtained From Bayesian Network Models,” M.S. - Master of Science, Middle East Technical University, 2023.