UC San Diego

To provide more optimal care at scale, health systems are changing the way in which healthcare is delivered. At the center of this changing landscape is a shift towards remote, continuous, and automated delivery of healthcare. This shift can lead to significant improvement in and scalability of at-home patient care for chronic diseases like hypertension and viral illnesses like COVID-19, while at the same time enabling significant savings in human and equipment resources. Wearable devices are an enabling technology making this shift in healthcare delivery possible due to the substantial amount of lifestyle and vitals data they can remotely collect. There is great opportunity for machine learning (ML) to assist in the remote and personalized delivery of care due to the large amount of data that is collected. In this dissertation, we present three applications of ML to enable personalized, remote health monitoring and care delivery. Chapter 1 presents a personalized deep learning approach to estimate blood pressure (BP) using the photoplethysmogram signal. Our approach enables continuous, noninvasive BP monitoring as compared to traditional methods which are either intermittent or invasive. To address the problem of limited personal data for individuals, we propose a transfer learning technique that achieves a mean absolute error of 3.52 and 2.20 mmHg for systolic and diastolic BP estimation, respectively. Chapter 2 describes a ML-based remote monitoring method to estimate patient recovery from COVID-19 symptoms using automatically collected wearable device data, instead of relying on manually collected symptom data. Our method achieves an F1-score of 0.88 when applying our Random Forest-based model personalization technique using weighted bootstrap aggregation. Chapter 3 presents the results of a single-arm nonrandomized trial which assessed the effectiveness of a fully digital, autonomous, and ML–based lifestyle coaching program on achieving BP control among adults with hypertension. 141 participants were monitored over 24 weeks and achieved an average systolic and diastolic BP decrease of 8.1 mmHg and 5.1 mmHg, respectively. Our research demonstrates the successful application of ML across various healthcare contexts. By harnessing wearable device data, we can facilitate more personalized and effective monitoring and interventions.