Ipek Ensari -Smooths, Splines, and the Chamber of Secrets - Demystifying Female Reproductive Health

Smooths, Splines, and the Chamber of Secrets - Demystifying Female Reproductive Health by Ipek Ensari
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Abstract: Chronic disorders affecting the female reproductive system often present diagnostic and treatment challenges due to their under-documentation within electronic health records and a lack of objective measures. Multimodal data from mobile health (mHealth) technologies can help close this gap by providing comprehensive patient profiles, insights into symptom patterns, and the interplay between symptomatic variance and personal factors. However, extracting meaningful insights from these noisy, high-dimensional data requires properly addressing their complex longitudinal patterns and irregular sampling.
To address these challenges, this talk will investigate generalized additive models (GAMs) using example cases from pelvic pain disorders (PPDs) - a cluster of conditions with many unknowns. To this end, we will employ smoothing functions and mixture models to reveal underlying trends and relationships that may not be immediately apparent. We will use real-life prospective patient data and nonparametric methods that can be used when there is uncertainty in the shape and patterns of the data.
Bio: Ipek Ensari, PhD, is an Assistant Professor at the Windreich Department of Artificial Intelligence and Human Health at the Icahn School of Medicine and the Hasso Plattner Institute of Digital Health at Mount Sinai. She investigates machine learning and digital health methods in the context of women’s reproductive health and disorders (e.g., endometriosis, dysmenorrhea, postpartum pain). She is currently the Principal Investigator of a NIH-funded study that aims to integrate functional data methods and distributed lag models to develop digital patient reported outcome measures for ambulatory monitoring and clinical decision-making. Ipek completed her doctorate at the University of Illinois at Urbana-Champaign and post-doctoral training at Columbia University in New York. Prior to Mount Sinai, she was as Associate Research Scientist at the Data Science Institute at Columbia University.
Twitter: / datatransformr
Presented at the 2024 New York R Conference (May 16, 2024)
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