Telma Pereira, University of Lisbon
Towards a reliable prediction of conversion from Mild Cognitive Impairment to Alzheimer’s Disease: a conformal prediction approach using time windows
Departmental Seminar: a talk for everyone giving a good introduction to a topic.
Predicting progression from a stage of Mild Cognitive Impairment to Alzheimer’s disease is a major pursuit in current dementia research. As a result, many prognostic models have emerged with the goal of supporting clinical decisions. Despite the efforts, the clinical application of such models has been hampered by: 1) the lack of a reliable assessment of the uncertainty of each prediction, and 2) not knowing the time to conversion. It is paramount for clinicians to know how much they can rely on the prediction made for a given patient (conversion or no conversion), and the time windows in case of conversion, in order to timely adjust the treatments. Supervised learning approaches to tackle these issues have been explored. We proposed a Time Windows approach to predict conversion of MCI to dementia, learning with patients stratified using time windows, thus fine-tuning the prognosis regarding the time to conversion. Moreover, to assess class uncertainty, we evaluated the Conformal Prediction approach on the task of making predictions with precise levels of confidence. These confidence measures can provide insight on the likelihood of each prediction.