Functional Data Methods for Characterizing Health and Disease Patterns via mHealth Technologies\ Visit https://rstats.ai/nyr/ to learn more. Abstract: Mobile and wearable health (mHealth) technologies are increasingly being used in research and clinical settings to monitor a wide range of outcomes, enabling unprecedented high-resolution perspective into patient health status. However, there remain gaps in how to leverage the large waves of incoming patient-generated health data (PGHD) from these technologies for their sense-making. Aggregating these data into scalar summary scores, though currently a common practice, fails to capture potentially meaningful within- and between-individuals variations. This talk will explore functional data analysis as an alternative approach to address some of these challenges related to PGHD. We will focus on a functional mixture model (FMM)-based clustering technique, where entire data curves are used as the unit of analysis, using examples from research with disease populations that are currently not well understood. Bio: Ipek Ensari is an Associate Research Scientist at the Data Science Institute at Columbia University in New York City. She investigates disease characterization (“phenotyping”) and patient symptom self-management using mobile Health and machine learning techniques for patient-generated health data. Her research focuses on women's health and reproductive conditions that are currently poorly understood, such as endometriosis. When not demystifying women's health, Ipek can be found building accessible K-12 data science education tools using R Shiny. Her latest collaboration toward this endeavor implements an interactive dashboard for teaching graphing and computational skills across various underserved school districts in the country. Twitter: https://twitter.com/datatransformr Presented at the 2022 New York R Conference (June 10, 2022)