4 papers
Toward Automated Detection of Documentation Inconsistencies in Electronic Health Records
Jian Lu, Panyu Chen, Miriam Treggiari +5
Objective: To characterize the kinds of internal documentation inconsistencies a general-domain large language model (LLM) can surface from real-world discharge summaries, and to i…
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…
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…