Louis J. Faust, Ph.D.

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SUMMARY

Louis Faust, Ph.D., is a data scientist and digital health researcher who leverages wearable health sensors to enhance healthcare delivery models and improve patient outcomes. His research applies novel machine-learning methodologies to the physiological and behavioral signals captured from these devices to construct interpretable and reproducible measures. With applications in clinical trials and remote monitoring, Dr. Faust's work uses these data streams to quantify, evaluate and predict patient health and well-being.

Focus areas

• Augmenting patient evaluations through health-sensor data. Wearable health sensors provide objective and continuous measures of patient physiology and behaviors in real time. Dr. Faust uses data science and machine-learning methodologies to learn interpretable patient representations from these data streams to facilitate evaluations of disease severity, treatment response and quality of life. His work has encompassed developing a machine-learning model to identify seizures in patients with epilepsy, as well as quantifying and tracking physical function in people with cancer to gauge their tolerance to chemotherapy.
• Digital biomarker discovery. Dr. Faust's research identifies novel manifestations uniquely available through the granular and longitudinal data of wearable health sensors. These digital biomarkers can enhance our understanding of patient health by providing novel perspectives and rich historical context. Dr. Faust's previous work in this area has included the discovery of a link between deviations from one's usual bedtime and elevated resting heart rate.
• Understanding adherence and abandonment of wearable health sensors. The ubiquity of wearable health sensors provides vast opportunities for gaining insights into patients' health. However, barriers such as device abandonment and nonadherence can compromise the ability to remotely monitor patients and jeopardize the validity of clinical trials. Dr. Faust investigates the driving factors behind these behaviors, as this knowledge may aid in the early identification of patients at risk of poor adherence, enabling targeted interventions that promote sustained patient engagement.

Significance to patient care

Wearable health sensors provide opportunities for passive data collection outside the hospital walls. Dr. Faust's research leverages this innovation by employing data science and machine-learning techniques on information collected through these devices for applications across the healthcare domain. Through the development of a machine-learning model to identify seizures in patients with epilepsy, Dr. Faust's research aims to use wrist-worn devices as a noninvasive alternative to implantable EEGs for seizure monitoring. Additionally, Dr. Faust's research seeks to minimize patient burden through reduced in-person visits and need to self-report data. Central to this is integrating objective measures from health sensors into clinical workflows, such as quantifying physical function in people receiving chemotherapy to assess treatment tolerance.