382 citations · 510 across the 17 of their papers we have counts for
17 papers
Exploring Large Language Models for Knowledge Graph Completion
Liang Yao, Jiazhen Peng, Chengsheng Mao +1
Knowledge graphs play a vital role in numerous artificial intelligence tasks, yet they frequently face the issue of incompleteness. In this study, we explore utilizing Large Langua…
IRTCI: Item Response Theory for Categorical Imputation
Adrienne Kline, Yuan Luo
Most datasets suffer from partial or complete missing values, which has downstream limitations on the available models on which to test the data and on any statistical inferences t…
AKI-BERT: a Pre-trained Clinical Language Model for Early Prediction of Acute Kidney Injury
Chengsheng Mao, Liang Yao, Yuan Luo
Acute kidney injury (AKI) is a common clinical syndrome characterized by a sudden episode of kidney failure or kidney damage within a few hours or a few days. Accurate early predic…
Distribution Preserving Graph Representation Learning
Chengsheng Mao, Yuan Luo
Graph neural network (GNN) is effective to model graphs for distributed representations of nodes and an entire graph. Recently, research on the expressive power of GNN attracted gr…
Constrained tensor factorization for computational phenotyping and mortality prediction in patients with cancer
Francisco Y Cai, Chengsheng Mao, Yuan Luo
Background: The increasing adoption of electronic health records (EHR) across the US has created troves of computable data, to which machine learning methods have been applied to e…
Non-Convex Optimization with Spectral Radius Regularization
Adam Sandler, Diego Klabjan, Yuan Luo
We develop regularization methods to find flat minima while training deep neural networks. These minima generalize better than sharp minima, yielding models outperforming baselines…
PANTHER: Pathway Augmented Nonnegative Tensor factorization for HighER-order feature learning
Yuan Luo, Chengsheng Mao
Genetic pathways usually encode molecular mechanisms that can inform targeted interventions. It is often challenging for existing machine learning approaches to jointly model genet…
Towards Expressive Graph Representation
Chengsheng Mao, Liang Yao, Yuan Luo
Graph Neural Network (GNN) aggregates the neighborhood of each node into the node embedding and shows its powerful capability for graph representation learning. However, most exist…
A Comparison of Pre-trained Vision-and-Language Models for Multimodal Representation Learning across Medical Images and Reports
Yikuan Li, Hanyin Wang, Yuan Luo
Joint image-text embedding extracted from medical images and associated contextual reports is the bedrock for most biomedical vision-and-language (V+L) tasks, including medical vis…
Open-Set Recognition with Gaussian Mixture Variational Autoencoders
Alexander Cao, Yuan Luo, Diego Klabjan
In inference, open-set classification is to either classify a sample into a known class from training or reject it as an unknown class. Existing deep open-set classifiers train exp…
Med2Meta: Learning Representations of Medical Concepts with Meta-Embeddings
Shaika Chowdhury, Chenwei Zhang, Philip S. Yu +1
Distributed representations of medical concepts have been used to support downstream clinical tasks recently. Electronic Health Records (EHR) capture different aspects of patients'…
Conditional Hierarchical Bayesian Tucker Decomposition for Genetic Data Analysis
Adam Sandler, Diego Klabjan, Yuan Luo
We analyze large, multi-dimensional, sparse counting data sets, finding unsupervised groups to provide unique insights into genetic data. We create gene and biological pathway grou…
KG-BERT: BERT for Knowledge Graph Completion
Liang Yao, Chengsheng Mao, Yuan Luo
Knowledge graphs are important resources for many artificial intelligence tasks but often suffer from incompleteness. In this work, we propose to use pre-trained language models fo…
Mixture-based Multiple Imputation Model for Clinical Data with a Temporal Dimension
Ye Xue, Diego Klabjan, Yuan Luo
The problem of missing values in multivariable time series is a key challenge in many applications such as clinical data mining. Although many imputation methods show their effecti…
Evaluating the Portability of an NLP System for Processing Echocardiograms: A Retrospective, Multi-site Observational Study
Prakash Adekkanattu, Guoqian Jiang, Yuan Luo +14
While natural language processing (NLP) of unstructured clinical narratives holds the potential for patient care and clinical research, portability of NLP approaches across multipl…
Identifying Sub-Phenotypes of Acute Kidney Injury using Structured and Unstructured Electronic Health Record Data with Memory Networks
Zhenxing Xu, Jingyuan Chou, Xi Sheryl Zhang +10
Acute Kidney Injury (AKI) is a common clinical syndrome characterized by the rapid loss of kidney excretory function, which aggravates the clinical severity of other diseases in a…
MedGCN: Medication recommendation and lab test imputation via graph convolutional networks
Chengsheng Mao, Liang Yao, Yuan Luo
Laboratory testing and medication prescription are two of the most important routines in daily clinical practice. Developing an artificial intelligence system that can automaticall…