Clinical Trial Active Learning
Zoe Fowler Kiran Kokilepersaud Mohit Prabhushankar Ghassan AlRegib
This paper presents a novel approach to active learning that takes into
account the non-independent and identically distributed (non-i.i.d.) structure
of a clinical trial setting. There exists two types of clinical trials:
retrospective and prospective. Retrospective clinical trials analyze data after
treatment has been performed; prospective clinical trials collect data as
treatment is ongoing. Typically, active learning approaches assume the dataset
is i.i.d. when selecting training samples; however, in the case of clinical
trials, treatment results in a dependency between the data collected at the
current and past visits. Thus, we propose prospective active learning to
overcome the limitations present in traditional active learning methods and
apply it to disease detection in optical coherence tomography (OCT) images,
where we condition on the time an image was collected to enforce the i.i.d.
assumption. We compare our proposed method to the traditional active learning
paradigm, which we refer to as retrospective in nature. We demonstrate that
prospective active learning outperforms retrospective active learning in two
different types of test settings.