4 citations · 7 across the 8 of their papers we have counts for
9 papers
A Statistical Framework for Data-Driven Discovery of Differential Performance in Clinical Risk Prediction Models
Aidan Neher, Julian Wolfson
Predictive models employing artificial intelligence (AI) and machine learning (ML) are increasingly being used for decision support in healthcare settings. These models may exhibit…
Reinforced Borrowing Framework: Leveraging Auxiliary Data for Individualized Inference
Ziyu Ji, Julian Wolfson
Increasingly during the past decade, researchers have sought to leverage auxiliary data for enhancing individualized inference. Many existing methods, such as multisource exchangea…
A flexible Bayesian framework for individualized inference via adaptive borrowing
Ziyu Ji, Julian Wolfson
The explosion in high-resolution data capture technologies in health has increased interest in making inferences about individual-level parameters. While technology may provide sub…
visTree: Visualization of Subgroups for a Decision Tree
Ashwini Venkatasubramaniam, Julian Wolfson
Decision trees are flexible prediction models which are constructed to quantify outcome-covariate relationships and characterize relevant population subgroups. However, the standar…
Assessing effect heterogeneity of a randomized treatment using conditional inference trees
Ashwini Venkatasubramaniam, Brandon Koch, Lauren Erickson +3
Treatment effect heterogeneity occurs when individual characteristics influence the effect of a treatment. We propose a novel approach that combines prognostic score matching and c…
MEBoost: Variable Selection in the Presence of Measurement Error
Benjamin Brown, Timothy Weaver, Julian Wolfson
We present a novel method for variable selection in regression models when covariates are measured with error. The iterative algorithm we propose, MEBoost, follows a path defined b…
Who's good this year? Comparing the Information Content of Games in the Four Major US Sports
Julian Wolfson, Joseph S. Koopmeiners
In the four major North American professional sports (baseball, basketball, football, and hockey), the primary purpose of the regular season is to determine which teams most deserv…
Data mining for censored time-to-event data: A Bayesian network model for predicting cardiovascular risk from electronic health record data
Sunayan Bandyopadhyay, Julian Wolfson, David M. Vock +5
Models for predicting the risk of cardiovascular events based on individual patient characteristics are important tools for managing patient care. Most current and commonly used ri…
A Naive Bayes machine learning approach to risk prediction using censored, time-to-event data
Julian Wolfson, Sunayan Bandyopadhyay, Mohamed Elidrisi +5
Predicting an individual's risk of experiencing a future clinical outcome is a statistical task with important consequences for both practicing clinicians and public health experts…