13 citations · 18 across the 4 of their papers we have counts for
7 papers
Electrocardiographic Deep Learning for Predicting Post-Procedural Mortality
David Ouyang, John Theurer, Nathan R. Stein +21
Background. Pre-operative risk assessments used in clinical practice are limited in their ability to identify risk for post-operative mortality. We hypothesize that electrocardiogr…
Causal Estimation with Functional Confounders
Aahlad Puli, Adler J. Perotte, Rajesh Ranganath
Causal inference relies on two fundamental assumptions: ignorability and positivity. We study causal inference when the true confounder value can be expressed as a function of the…
X-CAL: Explicit Calibration for Survival Analysis
Mark Goldstein, Xintian Han, Aahlad Puli +2
Survival analysis models the distribution of time until an event of interest, such as discharge from the hospital or admission to the ICU. When a model's predicted number of events…
Zero-Shot Clinical Acronym Expansion via Latent Meaning Cells
Griffin Adams, Mert Ketenci, Shreyas Bhave +2
We introduce Latent Meaning Cells, a deep latent variable model which learns contextualized representations of words by combining local lexical context and metadata. Metadata can r…
The Counterfactual -GAN
Amelia J. Averitt, Natnicha Vanitchanant, Rajesh Ranganath +1
Causal inference often relies on the counterfactual framework, which requires that treatment assignment is independent of the outcome, known as strong ignorability. Approaches to e…
Phenotype Inference with Semi-Supervised Mixed Membership Models
Victor Rodriguez, Adler Perotte
Disease phenotyping algorithms process observational clinical data to identify patients with specific diseases. Supervised phenotyping methods require significant quantities of exp…