Match tier
Listed
Presence
Current · Arkansas
Last published
2022
Sources
OpenAlex · ORCID
Refreshed
2026-08-08
Biography and Research Information
OverviewAI-generated summary
Joseph VanScoy's research focuses on the application of machine learning techniques to clinical data. His recent publications include "The h-ANN Model: Comprehensive Colonoscopy Concept Compilation using Combined Contextual Embeddings," "DeIDNER Model: A Neural Network Named Entity Recognition Model for Use in the De-identification of Clinical Notes," and "TAX-Corpus: Taxonomy based Annotations for Colonoscopy Evaluation." These works highlight his efforts in developing computational models for analyzing medical text and images, specifically in the domain of colonoscopy evaluation and the de-identification of patient information.
VanScoy collaborates with researchers at the University of Arkansas for Medical Sciences and Arkansas State University. His work has resulted in a total of three publications, with an h-index of 2 and 20 citations. He remains an active researcher, with his most recent publication in 2022.
Metrics
- h-index: 2
- Publications: 3
- Citations: 20
Selected Publications
-
TAX-Corpus: Taxonomy based Annotations for Colonoscopy Evaluation (2022)
-
DeIDNER Model: A Neural Network Named Entity Recognition Model for Use in the De-identification of Clinical Notes (2022)
-
The h-ANN Model: Comprehensive Colonoscopy Concept Compilation using Combined Contextual Embeddings (2022)
Collaboration Network
Top Collaborators
Shorabuddin Syed
University of Arkansas for Medical Sciences (US)
3 shared publications
- The h-ANN Model: Comprehensive Colonoscopy Concept Compilation using Combined Contextual Embeddings
- DeIDNER Model: A Neural Network Named Entity Recognition Model for Use in the De-identification of Clinical Notes
- TAX-Corpus: Taxonomy based Annotations for Colonoscopy Evaluation
Mahanazuddin Syed
University of Arkansas for Medical Sciences (US)
3 shared publications
- The h-ANN Model: Comprehensive Colonoscopy Concept Compilation using Combined Contextual Embeddings
- DeIDNER Model: A Neural Network Named Entity Recognition Model for Use in the De-identification of Clinical Notes
- TAX-Corpus: Taxonomy based Annotations for Colonoscopy Evaluation
Melody Greer
University of Arkansas for Medical Sciences
3 shared publications
In database
- The h-ANN Model: Comprehensive Colonoscopy Concept Compilation using Combined Contextual Embeddings
- DeIDNER Model: A Neural Network Named Entity Recognition Model for Use in the De-identification of Clinical Notes
- TAX-Corpus: Taxonomy based Annotations for Colonoscopy Evaluation
Sudeepa Bhattacharyya
Arkansas State University
3 shared publications
In database
- The h-ANN Model: Comprehensive Colonoscopy Concept Compilation using Combined Contextual Embeddings
- DeIDNER Model: A Neural Network Named Entity Recognition Model for Use in the De-identification of Clinical Notes
- TAX-Corpus: Taxonomy based Annotations for Colonoscopy Evaluation
Meredith Zozus
The University of Texas at San Antonio Health Science Center (US)
3 shared publications
- The h-ANN Model: Comprehensive Colonoscopy Concept Compilation using Combined Contextual Embeddings
- DeIDNER Model: A Neural Network Named Entity Recognition Model for Use in the De-identification of Clinical Notes
- TAX-Corpus: Taxonomy based Annotations for Colonoscopy Evaluation
Fred Prior
University of Arkansas for Medical Sciences
3 shared publications
In database
- The h-ANN Model: Comprehensive Colonoscopy Concept Compilation using Combined Contextual Embeddings
- DeIDNER Model: A Neural Network Named Entity Recognition Model for Use in the De-identification of Clinical Notes
- TAX-Corpus: Taxonomy based Annotations for Colonoscopy Evaluation
Adam Angel
2 shared publications
- The h-ANN Model: Comprehensive Colonoscopy Concept Compilation using Combined Contextual Embeddings
- TAX-Corpus: Taxonomy based Annotations for Colonoscopy Evaluation
Hafsa Bareen Syeda
University of Arkansas for Medical Sciences (US)
2 shared publications
- The h-ANN Model: Comprehensive Colonoscopy Concept Compilation using Combined Contextual Embeddings
- TAX-Corpus: Taxonomy based Annotations for Colonoscopy Evaluation
Carole Jennings
Tulane University (US)
2 shared publications
- The h-ANN Model: Comprehensive Colonoscopy Concept Compilation using Combined Contextual Embeddings
- TAX-Corpus: Taxonomy based Annotations for Colonoscopy Evaluation
Benjamin Tharian
University of Arkansas for Medical Sciences (US)
2 shared publications
- The h-ANN Model: Comprehensive Colonoscopy Concept Compilation using Combined Contextual Embeddings
- TAX-Corpus: Taxonomy based Annotations for Colonoscopy Evaluation
Kevin W. Sexton
University of Arkansas for Medical Sciences
1 shared publication
In database
- DeIDNER Model: A Neural Network Named Entity Recognition Model for Use in the De-identification of Clinical Notes
Farhan Kawsar
University of Arkansas for Medical Sciences
1 shared publication
In database
- DeIDNER Model: A Neural Network Named Entity Recognition Model for Use in the De-identification of Clinical Notes
Evan Olson
University of Arkansas for Medical Sciences (US)
1 shared publication
- DeIDNER Model: A Neural Network Named Entity Recognition Model for Use in the De-identification of Clinical Notes
Karan Patel
University of Arkansas for Medical Sciences (US)
1 shared publication
- DeIDNER Model: A Neural Network Named Entity Recognition Model for Use in the De-identification of Clinical Notes
Jake Erwin
University of Arkansas for Medical Sciences
1 shared publication
In database
- DeIDNER Model: A Neural Network Named Entity Recognition Model for Use in the De-identification of Clinical Notes
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