Abstract

Nutrition and obesity are major public health issues in the US and worldwide. There are significant disparities in diet quality and obesity among different populations. To better understand and address these disparities, artificial intelligence (AI) and machine learning (ML) methods can offer powerful tools for analyzing large and complex data sets, generating novel insights, and developing effective interventions. In this presentation I will review some of the statistical challenges in nutrition data analysis and opportunities pertaining to the use of AI/ML to answer health disparity questions. I will present real world applications of AI/ML methods for exploring the relationships between obesity with nutrition and other behaviors among children. I will also present preliminary findings on area-level food access, social determinants of health and obesity data. We will also discuss some of the challenges and limitations of these methods, such as data quality, ethical issues, and interpretability. We conclude by highlighting some of the future directions and opportunities for using AI/ML methods to advance the field of nutrition and obesity disparities research.

Speaker Bio

Dr. Salma Musaad is an Associate Professor of Pediatrics, Department of Pediatrics, Baylor College of Medicine (BCM). She has an MD from University of Khartoum, Sudan, and a Ph.D. in Epidemiology and Biostatistics from the University of Cincinnati, Department of Environmental Health, College of Medicine, with 14+ years of experience conducting clinical research in academia and clinical research organizations (industry). She joined the Children’s Nutrition Research center (CNRC) at BCM, where she is collaborating with other faculty at on grants pertaining to experimental and randomized trials as well as observational studies that investigate strategies to improve diet quality and reduce obesogenic behaviors. Dr Musaad is currently leading a Biostatistics and Data Research Core at the CNRC. She is the PI of the Biostatistics and Data Management Core for the NICHD’s 5-year P01 program titled “Leveraging passive objective assessment methods of preschooler’s media use to examine multiple paths of influence on sleep, executive function and weight status”. She also serves as the biostatistician for different projects, including the NIH Rare Disease Clinical Research Network’s Brittle Bone Disorders Consortium (NIH U54), Advancing Clinical Science in Pediatric Gastroparesis (NIDDK U01), Super Chef: Family Fun in the Kitchen (NHLBI R34), and is a Co-I in a multicenter observational study, the Environmental influences on Child Health Outcomes (ECHO) program for understanding the effects of environmental exposures on child cognition and development (NIH UH3).

In her previous roles, she worked in industry (Kendle and Battelle) where she was the lead biostatistician on multiple clinical trials spanning diverse subject areas. She also built and managed a biostatistical core in the Interdisciplinary Health Sciences Institute at the University of Illinois at Urbana-Champaign.

Her main research interests focus on the impact of diet quality and its environmental and behavioral determinants on chronic disorders including obesity, diabetes, asthma, and cancer as well as how they link with stress and inflammatory markers for cardiovascular disease. Dr Musaad is a member of the Network of Minority Health Research Investigators and a 2022 Fellow in the inaugural cohort of the Fellowship in Leadership within the artificial intelligence/machine learning (AI/ML) Consortium to Advance Health Equity and Researcher Diversity (AIM-AHEAD) program. She was a recipient of the Award for Excellence in Reviewing for the Journal of Nutrition Education and Behavior 2 years in a row (2021 and 2022). The Journal’s Editor In Chief and Executive Director, Society for Nutrition Education and Behavior stated that she was “… selected from over 800 reviewers based on your dedication to reviewing for JNEB and the quality of your reviews”. She also mentors clinical fellows regarding the design of proposals and analysis of existing data to support manuscript development and K awards.

To implement and expand on her AIM-AHEAD Fellowship in Leadership experience, Dr Musaad is developing a program for using AI/ML methods to advance equity in obesity prevention interventions and precision nutrition research, with an emphasis on contextual factors regarding social determinants of health and cultural attributes, and integration with high-dimensional data (e.g., ecological momentary assessments, wearables, gene expression signatures in response to nutrient intake and food components).

Dr. Musaad enjoys statistical programming in using SAS, R, MPlus, and Python, drawing, listening to classical music, and building forts with her children.