Detecting Atrial Fibrillation in ICU Telemetry Data with Weak Labels

Abstract

State of the art techniques for creating ML models in healthcare often require large quantities of clean, labelled data. However, many healthcare organizations lack the capacity to generate the large-scale annotations required to create and validate reliable labels. In this paper, we demonstrate how raw data from an information-rich area of care can be exploited without the need for mass manual annotation via the use of weak labels. We evaluate the AF Detection with Weak Labels proposed framework on telemetry data from the intensive care unit for application of atrial fibrillation (AF) detection. We generate an in-house dataset of over 60,000 ECG segments with weak labels, derived from a model trained on publicly available data. We then show that building a deep learning model based on these weakly generated labels can significantly improve (more than 30%) the performance of AF detection in comparison with only using limited expert-annotated ground truth labels. We further demonstrate how weakly supervised learning techniques can be used to augment and control the level of noise in these weak labels. Lastly, we explore how supervised fine-tuning effects the performance of these models and discuss the viability of leveraging weak labels for large-scale atrial fibrillation detection and identification.