8 citations · 9 across the 5 of their papers we have counts for
5 papers
Bootstrapping Your Own Positive Sample: Contrastive Learning With Electronic Health Record Data
Tingyi Wanyan, Jing Zhang, Ying Ding +3
Electronic Health Record (EHR) data has been of tremendous utility in Artificial Intelligence (AI) for healthcare such as predicting future clinical events. These tasks, however, o…
Contrastive Learning Improves Critical Event Prediction in COVID-19 Patients
Tingyi Wanyan, Hossein Honarvar, Suraj K. Jaladanki +13
Machine Learning (ML) models typically require large-scale, balanced training data to be robust, generalizable, and effective in the context of healthcare. This has been a major is…
Deep Learning with Heterogeneous Graph Embeddings for Mortality Prediction from Electronic Health Records
Tingyi Wanyan, Hossein Honarvar, Ariful Azad +2
Computational prediction of in-hospital mortality in the setting of an intensive care unit can help clinical practitioners to guide care and make early decisions for interventions.…
Biomedical Knowledge Graph Refinement and Completion using Graph Representation Learning and Top-K Similarity Measure
Islam Akef Ebeid, Majdi Hassan, Tingyi Wanyan +3
Knowledge Graphs have been one of the fundamental methods for integrating heterogeneous data sources. Integrating heterogeneous data sources is crucial, especially in the biomedica…
Attribute2vec: Deep Network Embedding Through Multi-Filtering GCN
Tingyi Wanyan, Chenwei Zhang, Ariful Azad +3
We present a multi-filtering Graph Convolution Neural Network (GCN) framework for network embedding task. It uses multiple local GCN filters to do feature extraction in every propa…