6 citations · 6 across the 9 of their papers we have counts for
11 papers
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…
Borrowing From the Future: Enhancing Early Risk Assessment through Contrastive Learning
Minghui Sun, Matthew M. Engelhard, Benjamin A. Goldstein
Risk assessments for a pediatric population are often conducted across multiple stages. For example, clinicians may evaluate risks prenatally, at birth, and during Well-Child visit…
FairPOT: Balancing AUC Performance and Fairness with Proportional Optimal Transport
Pengxi Liu, Yi Shen, Matthew M. Engelhard +4
Fairness metrics utilizing the area under the receiver operator characteristic curve (AUC) have gained increasing attention in high-stakes domains such as healthcare, finance, and…
CLEAR: Unlearning Spurious Style-Content Associations with Contrastive LEarning with Anti-contrastive Regularization
Minghui Sun, Benjamin A. Goldstein, Matthew M. Engelhard
Learning representations unaffected by superficial characteristics is important to ensure that shifts in these characteristics at test time do not compromise downstream prediction…
Infinite hierarchical contrastive clustering for personal digital envirotyping
Ya-Yun Huang, Joseph McClernon, Jason A. Oliver +1
Daily environments have profound influence on our health and behavior. Recent work has shown that digital envirotyping, where computer vision is applied to images of daily environm…
MALADE: Orchestration of LLM-powered Agents with Retrieval Augmented Generation for Pharmacovigilance
Jihye Choi, Nils Palumbo, Prasad Chalasani +4
In the era of Large Language Models (LLMs), given their remarkable text understanding and generation abilities, there is an unprecedented opportunity to develop new, LLM-based meth…