6 citations · 6 across the 9 of their papers we have counts for
6 papers · 1 filter
When are time-to-event models a waste of time? Bridging mixture cure models and positive-unlabeled learning for binary classification under right-censoring
Qin Weng, Matthew M. Engelhard
In clinical settings, predicting binary outcomes is often complicated by right-censoring, which prevents us from distinguishing individuals in whom the event never occurs (i.e., ne…
Integrating Neural Encoders in Bayesian Generalized Linear Mixed Models for Multimodal Data
Yuankang Zhao, Youngsoo Baek, Felipe A. Medeiros +2
Scalable Bayesian inference for generalized linear mixed models (GLMMs) provides uncertainty-aware analysis of correlated longitudinal data, but existing scalable approaches largel…
Multimodal Training to Unimodal Deployment: Leveraging Unstructured Data During Training to Optimize Structured Data Only Deployment
Zigui Wang, Minghui Sun, Jiang Shu +3
Unstructured Electronic Health Record (EHR) data, such as clinical notes, contain clinical contextual observations that are not directly reflected in structured data fields. This a…
Double Variable Importance Matching to Estimate Distinct Causal Effects on Event Probability and Timing
Yuqi Li, Quinn Lanners, Matthew M. Engelhard
In many clinical contexts, estimating effects of treatment in time-to-event data is complicated not only by confounding, censoring, and heterogeneity, but also by the presence of a…
Extending ROC Analysis to Uncertainty-Aware Risk Prediction with an Interval-Based AUC (iAUC)
Yuqi Li, Matthew M. Engelhard
In high-stakes risk prediction, interval-valued predictions provide a natural way to represent predictive uncertainty. However, standard evaluation tools such as the receiver opera…
NEST: Nested Event Stream Transformer for Sequences of Multisets
Minghui Sun, Haoyu Gong, Xingyu You +3
Event stream data often exhibit hierarchical structure in which multiple events co-occur, resulting in a sequence of multisets (i.e., bags of events). In electronic health records…