6 citations · 10 across the 11 of their papers we have counts for
5 papers · 1 filter
A tutorial on discovering and quantifying the effect of latent causal sources of multimodal EHR data
Marco Barbero-Mota, Eric V. Strobl, John M. Still +2
We provide an accessible description of a peer-reviewed generalizable causal machine learning pipeline to (i) discover latent causal sources of large-scale electronic health record…
Self-supervised learning of imaging and clinical signatures using a multimodal joint-embedding predictive architecture
Thomas Z. Li, Aravind R. Krishnan, Lianrui Zuo +5
The development of multimodal models for pulmonary nodule diagnosis is limited by the scarcity of labeled data and the tendency for these models to overfit on the training distribu…
Lifespan Pancreas Morphology for Control vs Type 2 Diabetes using AI on Largescale Clinical Imaging
Lucas W. Remedios, Chloe Cho, Trent M. Schwartz +11
Purpose: Understanding how the pancreas changes is critical for detecting deviations in type 2 diabetes and other pancreatic disease. We measure pancreas size and shape using morph…
Cryptogenic stroke and migraine: using probabilistic independence and machine learning to uncover latent sources of disease from the electronic health record
Joshua W. Betts, John M. Still, Thomas A. Lasko
Migraine is a common but complex neurological disorder that doubles the lifetime risk of cryptogenic stroke (CS). However, this relationship remains poorly characterized, and few c…
A data-driven approach to discover and quantify systemic lupus erythematosus etiological heterogeneity from electronic health records
Marco Barbero Mota, John M. Still, Jorge L. Gamboa +4
Systemic lupus erythematosus (SLE) is a complex heterogeneous disease with many manifestational facets. We propose a data-driven approach to discover probabilistic independent sour…