Clinical Trial Active Learning
This work addresses the challenge of active learning in clinical trial settings, which is incremental as it adapts existing methods to handle non-i.i.d. data dependencies.
The paper tackles the problem of applying active learning to clinical trials by addressing the non-i.i.d. data structure, proposing a prospective active learning method that conditions on time to enforce i.i.d. assumptions, and demonstrates it outperforms traditional retrospective active learning in disease detection using OCT images.
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.