Refereed

CS030 Machine Learning Methods for Causal Inference in Health Disparities and Disease

Thu, Apr 30, 3:45 PM - 5:15 PM Room Milwaukee B Hyatt Regency Milwaukee

Data Science Applications

3 Presentations

Machine Learning–Assisted High-Dimensional Causal Mediation to Investigate Environmental Drivers of Racial Inequities in Preterm Birth

3:50 PM - 4:15 PM

Co-authors: Laleh Jamshidi (University of Wisconsin-Milwaukee), Amy Kalkbrenner (University of Wisconsin-Milwaukee), Laleh Jamshidi (University of Wisconsin-Milwaukee)

Causal mediation double machine learning Targeted Maximum Likelihood Estimation (TMLE)high-dimensional mediators preterm birth health inequities

Predict-Then-Debias for Exploring the Relationship Between Retinal Layer Thickness and Brain Substructure Volumes in Multiple Sclerosis

4:15 PM - 4:40 PM

Co-authors: Chau Nguyen (Portland State University), Angeliki Filippatou (Department of Neurology, Johns Hopkins University), Chau Nguyen (Portland State University), Brenna McCormack (Department of Neurology, Johns Hopkins University), Anna Baccheti (Department of Neurology, Johns Hopkins University), Ting-Yi Lin (Department of Neurology, Johns Hopkins University), Kathryn Fitzgerald (Department of Neurology, Johns Hopkins University), Shuwen Wei (Whiting School of Engineering, Johns Hopkins University), Aaron Carass (Whiting School of Engineering, Johns Hopkins University), Evan Johnson (Department of Neurology, Johns Hopkins University), Shiv Saidha (Department of Neurology, Johns Hopkins University), Peter Calabresi (Department of Neurology, Johns Hopkins University), Jerry Prince (Whiting School of Engineering, Johns Hopkins University), Giulia Moretti (Department of Neurology, Johns Hopkins University), Elias Sotirchos (Department of Neurology, Johns Hopkins University), Bruno Jedynak (Portland State University - Portland, OR)

Predict-then-debias Optical Coherence Tomography (OCT)Magnetic Resonance Imaging (MRI)Multiple Sclerosis Prediction-powered inference Machine learning for science

A Comprehensive Analysis on Association of Metabolic Syndrome and Circadian Syndrome with Cancer Outcomes in US Population Using Explainable Machine Learning

4:40 PM - 5:05 PM

Co-authors: Shakeel Ahmed, Rajkumar Lakshmanaswamy (Department of Molecular and Translational Medicine, TTUHSC), Shakeel Ahmed

Explainable Machine Learning;Cancer Outcomes Metabolic Stressor Survey-weighted Analysis Metabolic Syndrome Circadian Syndrome