Biography and Research Information
OverviewAI-generated summary
Joseph VanScoy's research focuses on the application of machine learning and natural language processing 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 interest in developing computational models for analyzing and extracting information from medical texts, specifically in the context of colonoscopy evaluations and the de-identification of patient notes.
VanScoy has collaborated with researchers from the University of Arkansas for Medical Sciences, including Melody Greer and Fred Prior, as well as Sudeepa Bhattacharyya from Arkansas State University. His scholarly output includes 3 publications with an h-index of 2 and 19 total citations, indicating recent activity in his field.
Metrics
- h-index: 2
- Publications: 3
- Citations: 19
Selected Publications
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TAX-Corpus: Taxonomy based Annotations for Colonoscopy Evaluation (2022)
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DeIDNER Model: A Neural Network Named Entity Recognition Model for Use in the De-identification of Clinical Notes (2022)
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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 (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
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 Health Science Center at San Antonio (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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