12 citations · 24 across the 12 of their papers we have counts for
12 papers
Probabilistic Clustering using Shared Latent Variable Model for Assessing Alzheimers Disease Biomarkers
Yizhen Xu, Scott Zeger, Zheyu Wang
The preclinical stage of many neurodegenerative diseases can span decades before symptoms become apparent. Understanding the sequence of preclinical biomarker changes provides a cr…
The Shaky Foundations of Clinical Foundation Models: A Survey of Large Language Models and Foundation Models for EMRs
Michael Wornow, Yizhe Xu, Rahul Thapa +6
The successes of foundation models such as ChatGPT and AlphaFold have spurred significant interest in building similar models for electronic medical records (EMRs) to improve patie…
Causal Inference using Multivariate Generalized Linear Mixed-Effects Models with Longitudinal Data
Yizhen Xu, Jisoo Kim, Laura K. Hummers +2
Dynamic prediction of causal effects under different treatment regimes conditional on an individual's characteristics and longitudinal history is an essential problem in precision…
MOTOR: A Time-To-Event Foundation Model For Structured Medical Records
Ethan Steinberg, Jason Fries, Yizhe Xu +1
We present a self-supervised, time-to-event (TTE) foundation model called MOTOR (Many Outcome Time Oriented Representations) which is pretrained on timestamped sequences of events…
Clinical Utility Gains from Incorporating Comorbidity and Geographic Location Information into Risk Estimation Equations for Atherosclerotic Cardiovascular Disease
Yizhe Xu, Agata Foryciarz, Ethan Steinberg +1
Objective: There are several efforts to re-learn the 2013 ACC/AHA pooled cohort equations (PCE) for patients with specific comorbidities and geographic locations. With over 363 cus…
Treatment Heterogeneity for Survival Outcomes
Yizhe Xu, Nikolaos Ignatiadis, Erik Sverdrup +3
Estimation of conditional average treatment effects (CATEs) plays an essential role in modern medicine by informing treatment decision-making at a patient level. Several metalearne…
An Efficient Approach for Optimizing the Cost-effective Individualized Treatment Rule Using Conditional Random Forest
Yizhe Xu, Tom H. Greene, Adam P. Bress +7
Evidence from observational studies has become increasingly important for supporting healthcare policy making via cost-effectiveness (CE) analyses. Similar as in comparative effect…
Calibration Error for Heterogeneous Treatment Effects
Yizhe Xu, Steve Yadlowsky
Recently, many researchers have advanced data-driven methods for modeling heterogeneous treatment effects (HTEs). Even still, estimation of HTEs is a difficult task -- these method…
Net benefit, calibration, threshold selection, and training objectives for algorithmic fairness in healthcare
Stephen R. Pfohl, Yizhe Xu, Agata Foryciarz +3
A growing body of work uses the paradigm of algorithmic fairness to frame the development of techniques to anticipate and proactively mitigate the introduction or exacerbation of h…
A comparison of approaches to improve worst-case predictive model performance over patient subpopulations
Stephen R. Pfohl, Haoran Zhang, Yizhe Xu +3
Predictive models for clinical outcomes that are accurate on average in a patient population may underperform drastically for some subpopulations, potentially introducing or reinfo…
Multiply Robust Causal Mediation Analysis with Continuous Treatments
Yizhen Xu, AmirEmad Ghassami, Numair Sani +1
In many applications, researchers are interested in the direct and indirect causal effects of a treatment or exposure on an outcome of interest. Mediation analysis offers a rigorou…
Using public clinical trial reports to evaluate observational study methods
Ethan Steinberg, Nikolaos Ignatiadis, Steve Yadlowsky +2
Observational studies are valuable for estimating the effects of various medical interventions, but are notoriously difficult to evaluate because the methods used in observational…