33 citations · 102 across the 13 of their papers we have counts for
5 papers · 1 filter
Feature Robustness in Non-stationary Health Records: Caveats to Deployable Model Performance in Common Clinical Machine Learning Tasks
Bret Nestor, Matthew B. A. McDermott, Willie Boag +5
When training clinical prediction models from electronic health records (EHRs), a key concern should be a model's ability to sustain performance over time when deployed, even as ca…
What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use
Sana Tonekaboni, Shalmali Joshi, Melissa D McCradden +1
Translating machine learning (ML) models effectively to clinical practice requires establishing clinicians' trust. Explainability, or the ability of an ML model to justify its outc…
Reducing Adversarial Example Transferability Using Gradient Regularization
George Adam, Petr Smirnov, Benjamin Haibe-Kains +1
Deep learning algorithms have increasingly been shown to lack robustness to simple adversarial examples (AdvX). An equally troubling observation is that these adversarial examples…
The False Positive Control Lasso
Erik Drysdale, Yingwei Peng, Timothy P. Hanna +2
In high dimensional settings where a small number of regressors are expected to be important, the Lasso estimator can be used to obtain a sparse solution vector with the expectatio…
Dynamic Measurement Scheduling for Event Forecasting using Deep RL
Chun-Hao Chang, Mingjie Mai, Anna Goldenberg
Imagine a patient in critical condition. What and when should be measured to forecast detrimental events, especially under the budget constraints? We answer this question by deep r…