A novel reject option applied to sleep stage scoring

Abstract

Sleep stage scoring is an essential component of diagnosing sleep disorders. Unfortunately, it is a time-intensive task that requires clinical experts to annotate an entire night's recording for each patient. Therefore, machine learned models offer the potential to alleviate this burden by automating this task. While learned models achieve acceptable accuracy on curated data, these models still produce highly inaccurate scorings for certain patients when deployed in medical centers. This is because particular subsets of the population may not be adequately represented in the data used to train the model. For example, data are not easily accessible (e.g., a given age group like children) or are hard or impossible to collect (e.g., patients with a rare disease or previously unknown pathology). This creates trust issues as incorrect scorings can have severe consequences such as undetected diseases. To address this, we propose augmenting an existing model with a reject option which enables it to abstain from making predictions if the model is at an elevated risk of making a mistake. We show that traditional rejection frameworks can systematically be too cautious in certain circumstances and abstain even when the model can make good predictions. We propose a solution by considering both the data distribution and the model predictions. We demonstrate the efficacy of our method on a real-world sleep scoring use case. Moreover, we found that our approach leads to improved performance on several publicly available benchmarks.