10 citations · 31 across the 13 of their papers we have counts for
14 papers
Multimodal domain adaptation under label shift and blockwise missing modalities
Zebin Wang, Ziang Dou, Molei Liu +1
Multimodal domain adaptation uses labeled source datasets to predict outcomes in an unlabeled target population. Here, different sources may observe different subsets of modalities…
Knowledge Graph Embedding with Electronic Health Records Data via Latent Graphical Block Model
Junwei Lu, Jin Yin, Tianxi Cai
Due to the increasing adoption of electronic health records (EHR), large scale EHRs have become another rich data source for translational clinical research. Despite its potential,…
LATTE: Label-efficient Incident Phenotyping from Longitudinal Electronic Health Records
Jun Wen, Jue Hou, Clara-Lea Bonzel +14
Electronic health record (EHR) data are increasingly used to support real-world evidence (RWE) studies. Yet its ability to generate reliable RWE is limited by the lack of readily a…
Efficient Modeling of Surrogates to Improve Multi-source High-dimensional Integrative Regression
Yue Liu, Molei Liu, Zijian Guo +1
Surrogate variables play an important role in various fields due to the scarcity or absence of gold standard labels. We develop a novel approach named SASH for Surrogate-Assisted a…
Robust Inference for Federated Meta-Learning
Zijian Guo, Xiudi Li, Larry Han +1
Synthesizing information from multiple data sources is critical to ensure knowledge generalizability. Integrative analysis of multi-source data is challenging due to the heterogene…
Harnessing electronic health records for real-world evidence
Jue Hou, Rachel Zhao, Jessica Gronsbell +13
While randomized controlled trials (RCTs) are the gold-standard for establishing the efficacy and safety of a medical treatment, real-world evidence (RWE) generated from real-world…
Towards Optimal Use of Surrogate Markers to Improve Power
Xuan Wang, Layla Parast, Lu Tian +1
Motivated by increasing pressure for decision makers to shorten the time required to evaluate the efficacy of a treatment such that treatments deemed safe and effective can be made…
Consensus Knowledge Graph Learning via Multi-view Sparse Low Rank Block Model
Tianxi Cai, Dong Xia, Luwan Zhang +1
Network analysis has been a powerful tool to unveil relationships and interactions among a large number of objects. Yet its effectiveness in accurately identifying important node-n…
Semi-supervised Triply Robust Inductive Transfer Learning
Tianxi Cai, Mengyan Li, Molei Liu
In this work, we propose a Semi-supervised Triply Robust Inductive transFer LEarning (STRIFLE) approach, which integrates heterogeneous data from a label-rich source population and…
Semi-supervised Transfer Learning for Evaluation of Model Classification Performance
Linshanshan Wang, Xuan Wang, Katherine P. Liao +1
In modern machine learning applications, frequent encounters of covariate shift and label scarcity have posed challenges to robust model training and evaluation. Numerous transfer…
Doubly Robust Augmented Model Accuracy Transfer Inference with High Dimensional Features
Doudou Zhou, Molei Liu, Mengyan Li +1
Due to label scarcity and covariate shift happening frequently in real-world studies, transfer learning has become an essential technique to train models generalizable to some targ…
Federated Offline Reinforcement Learning
Doudou Zhou, Yufeng Zhang, Aaron Sonabend-W +3
Evidence-based or data-driven dynamic treatment regimes are essential for personalized medicine, which can benefit from offline reinforcement learning (RL). Although massive health…
Multimodal Learning on Graphs for Disease Relation Extraction
Yucong Lin, Keming Lu, Sheng Yu +2
Objective: Disease knowledge graphs are a way to connect, organize, and access disparate information about diseases with numerous benefits for artificial intelligence (AI). To crea…
Federated Adaptive Causal Estimation (FACE) of Target Treatment Effects
Larry Han, Jue Hou, Kelly Cho +2
Federated learning of causal estimands may greatly improve estimation efficiency by leveraging data from multiple study sites, but robustness to heterogeneity and model misspecific…