34 citations · 63 across the 23 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…
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…
Unsupervised Discovery of Clinical Disease Signatures Using Probabilistic Independence
Thomas A. Lasko, John M. Still, Thomas Z. Li +5
Insufficiently precise diagnosis of clinical disease is likely responsible for many treatment failures, even for common conditions and treatments. With a large enough dataset, it m…
Why Do Probabilistic Clinical Models Fail To Transport Between Sites?
Thomas A. Lasko, Eric V. Strobl, William W. Stead
The rising popularity of artificial intelligence in healthcare is highlighting the problem that a computational model achieving super-human clinical performance at its training sit…
Semi-supervised Contrastive Learning Using Partial Label Information
Colin B. Hansen, Vishwesh Nath, Diego A. Mesa +3
In semi-supervised learning, information from unlabeled examples is used to improve the model learned from labeled examples. In some learning problems, partial label information ca…