78 citations · 149 across the 13 of their papers we have counts for
13 papers
Probabilistic Shapley Value Modeling and Inference
Mert Ketenci, Iñigo Urteaga, Victor Alfonso Rodriguez +2
We propose probabilistic Shapley inference (PSI), a novel probabilistic framework to model and infer sufficient statistics of feature attributions in flexible predictive models, vi…
Maximum Likelihood Estimation of Flexible Survival Densities with Importance Sampling
Mert Ketenci, Shreyas Bhave, Noémie Elhadad +1
Survival analysis is a widely-used technique for analyzing time-to-event data in the presence of censoring. In recent years, numerous survival analysis methods have emerged which s…
Accurate and Scalable Stochastic Gaussian Process Regression via Learnable Coreset-based Variational Inference
Mert Ketenci, Adler Perotte, Noémie Elhadad +1
We introduce a novel stochastic variational inference method for Gaussian process () regression, by deriving a posterior over a learnable set of coresets: i.e., over…
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…
CEHR-BERT: Incorporating temporal information from structured EHR data to improve prediction tasks
Chao Pang, Xinzhuo Jiang, Krishna S Kalluri +4
Embedding algorithms are increasingly used to represent clinical concepts in healthcare for improving machine learning tasks such as clinical phenotyping and disease prediction. Re…
Inverse-Weighted Survival Games
Xintian Han, Mark Goldstein, Aahlad Puli +3
Deep models trained through maximum likelihood have achieved state-of-the-art results for survival analysis. Despite this training scheme, practitioners evaluate models under other…
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…
Multiple Causal Inference with Latent Confounding
Rajesh Ranganath, Adler Perotte
Causal inference from observational data requires assumptions. These assumptions range from measuring confounders to identifying instruments. Traditionally, causal inference assump…
Deep Survival Analysis
Rajesh Ranganath, Adler Perotte, Noémie Elhadad +1
The electronic health record (EHR) provides an unprecedented opportunity to build actionable tools to support physicians at the point of care. In this paper, we investigate surviva…