The practice of medicine is characterized by complex situations that evoke uncertainty.
Uncertainty has implications for the quality and costs of health care, thus emphasizing
the importance of identifying its the main causes.
Uncertainty can be manifested through human behaviour. Accordingly, in this dissertation,
a machine learning model that detects events of uncertainty based on mouse
cursor movements was created. To do so, 79 participants answered an online survey while
the mouse data was being tracked. This data was used to extract meaningful features that
allowed model testing and training after a feature selection stage. With the implementation
of a Logistic Regression, and applying a k-fold cross-validation method, the model
achieved an estimated performance of 81%.
It was found that, during moments of uncertainty, the number of horizontal direction
inversions increases and the mouse cursor travels higher distances. Moreover, items that
evoke uncertainty are associated to longer interaction times and a higher number of visits.
Subsequently, the model was applied to a medical decision making task performed
by 8 physicians, in order to understand whether it might be applied in different contexts
or not. The results were consistent with the task design.
To better understand the nature of uncertainty, its relationship with personality was
explored. Regarding the clinical task, it was found a slight tendency of uncertainty to
increase with Neuroticism.
In the future, the created model may be used to help physicians understand their
main difficulties
