A Machine Learning Approach to Detecting Low Medication State with Wearable Technologies

Medication adherence is a critical component and implicit assumption of the patient life cycle that is often violated, incurring financial and medical costs to both patients and the medical system at large. As obstacles to medication adherence are complex and varied, approaches to overcome them must themselves be multifaceted. This paper demonstrates one such approach using sensor data recorded by an Apple Watch to detect low counts of pill medication in standard prescription bottles. We use distributed computing on a cloud-based platform to efficiently process large volumes of high-frequency data and train a Gradient Boosted Tree machine learning model. Our final model yielded average cross-validated accuracy and F1 scores of 80.27% and 80.22%, respectively. We conclude this paper with two use cases in which wearable devices such as the Apple Watch can contribute to efforts to improve patient medication adherence.