Clinical Trial Active Learning

TL;DR

This paper introduces a new prospective active learning method tailored for clinical trials, accounting for data dependencies over time, and demonstrates its superiority over traditional retrospective methods in OCT disease detection.

Contribution

The paper proposes a novel prospective active learning approach that addresses non-i.i.d. data in clinical trials, improving sample selection for disease detection tasks.

Findings

Prospective active learning outperforms retrospective methods in OCT disease detection.

The method effectively handles temporal dependencies in clinical trial data.

Experimental results show improved accuracy with the proposed approach.

Abstract

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.