54 citations · 158 across the 16 of their papers we have counts for
17 papers
Weakly-Supervised Scientific Document Classification via Retrieval-Augmented Multi-Stage Training
Ran Xu, Yue Yu, Joyce C. Ho +1
Scientific document classification is a critical task for a wide range of applications, but the cost of obtaining massive amounts of human-labeled data can be prohibitive. To addre…
A Review on Knowledge Graphs for Healthcare: Resources, Applications, and Promises
Hejie Cui, Jiaying Lu, Ran Xu +14
This comprehensive review aims to provide an overview of the current state of Healthcare Knowledge Graphs (HKGs), including their construction, utilization models, and applications…
PGB: A PubMed Graph Benchmark for Heterogeneous Network Representation Learning
Eric W Lee, Joyce C Ho
There has been rapid growth in biomedical literature, yet capturing the heterogeneity of the bibliographic information of these articles remains relatively understudied. Although g…
Neighborhood-Regularized Self-Training for Learning with Few Labels
Ran Xu, Yue Yu, Hejie Cui +5
Training deep neural networks (DNNs) with limited supervision has been a popular research topic as it can significantly alleviate the annotation burden. Self-training has been succ…
MedDiff: Generating Electronic Health Records using Accelerated Denoising Diffusion Model
Huan He, Shifan Zhao, Yuanzhe Xi +1
Due to patient privacy protection concerns, machine learning research in healthcare has been undeniably slower and limited than in other application domains. High-quality, realisti…
MULTIPAR: Supervised Irregular Tensor Factorization with Multi-task Learning
Yifei Ren, Jian Lou, Li Xiong +3
Tensor factorization has received increasing interest due to its intrinsic ability to capture latent factors in multi-dimensional data with many applications such as recommender sy…
Communication Efficient Generalized Tensor Factorization for Decentralized Healthcare Networks
Jing Ma, Qiuchen Zhang, Jian Lou +3
Tensor factorization has been proved as an efficient unsupervised learning approach for health data analysis, especially for computational phenotyping, where the high-dimensional E…
Spatio-Temporal Tensor Sketching via Adaptive Sampling
Jing Ma, Qiuchen Zhang, Joyce C. Ho +1
Mining massive spatio-temporal data can help a variety of real-world applications such as city capacity planning, event management, and social network analysis. The tensor represen…
Domain-Guided Task Decomposition with Self-Training for Detecting Personal Events in Social Media
Payam Karisani, Joyce C. Ho, Eugene Agichtein
Mining social media content for tasks such as detecting personal experiences or events, suffer from lexical sparsity, insufficient training data, and inventive lexicons. To reduce…
TASTE: Temporal and Static Tensor Factorization for Phenotyping Electronic Health Records
Ardavan Afshar, Ioakeim Perros, Haesun Park +5
Phenotyping electronic health records (EHR) focuses on defining meaningful patient groups (e.g., heart failure group and diabetes group) and identifying the temporal evolution of p…
Self-Attention Based Molecule Representation for Predicting Drug-Target Interaction
Bonggun Shin, Sungsoo Park, Keunsoo Kang +1
Predicting drug-target interactions (DTI) is an essential part of the drug discovery process, which is an expensive process in terms of time and cost. Therefore, reducing DTI cost…
Privacy-Preserving Tensor Factorization for Collaborative Health Data Analysis
Jing Ma, Qiuchen Zhang, Jian Lou +3
Tensor factorization has been demonstrated as an efficient approach for computational phenotyping, where massive electronic health records (EHRs) are converted to concise and meani…
PIVETed-Granite: Computational Phenotypes through Constrained Tensor Factorization
Jette Henderson, Bradley A. Malin, Joyce C. Ho +1
It has been recently shown that sparse, nonnegative tensor factorization of multi-modal electronic health record data is a promising approach to high-throughput computational pheno…
PaloBoost: An Overfitting-robust TreeBoost with Out-of-Bag Sample Regularization Techniques
Yubin Park, Joyce C. Ho
Stochastic Gradient TreeBoost is often found in many winning solutions in public data science challenges. Unfortunately, the best performance requires extensive parameter tuning an…
COPA: Constrained PARAFAC2 for Sparse & Large Datasets
Ardavan Afshar, Ioakeim Perros, Evangelos E. Papalexakis +3
PARAFAC2 has demonstrated success in modeling irregular tensors, where the tensor dimensions vary across one of the modes. An example scenario is modeling treatments across a set o…
Phenotyping using Structured Collective Matrix Factorization of Multi--source EHR Data
Suriya Gunasekar, Joyce C. Ho, Joydeep Ghosh +5
The increased availability of electronic health records (EHRs) have spearheaded the initiative for precision medicine using data driven approaches. Essential to this effort is the…
Risk Prediction of a Multiple Sclerosis Diagnosis
Joyce C. Ho, Joydeep Ghosh, KP Unnikrishnan
Multiple sclerosis (MS) is a chronic autoimmune disease that affects the central nervous system. The progression and severity of MS varies by individual, but it is generally a disa…