Match tier
Listed
Presence
Current · Arkansas
Last published
2022
Sources
OpenAlex · ORCID
Refreshed
2026-08-18
Biography and Research Information
OverviewAI-generated summary
Jake Erwin's research focuses on the application of machine learning models to clinical data, specifically for the de-identification of clinical notes. His work involves developing and evaluating neural network-based named entity recognition (NER) models for this purpose. This research is situated within the broader field of health sciences and medical informatics.
Erwin has one publication, the "DeIDNER Model: A Neural Network Named Entity Recognition Model for Use in the De-identification of Clinical Notes," published in 2022. His scholarly output has garnered 7 citations and an h-index of 1. He collaborates with researchers at the University of Arkansas for Medical Sciences, including Fred Prior, Kevin W. Sexton, Farhan Kawsar, and Joseph VanScoy, with whom he shares one publication.
Metrics
- h-index: 1
- Publications: 1
- Citations: 7
Selected Publications
-
DeIDNER Model: A Neural Network Named Entity Recognition Model for Use in the De-identification of Clinical Notes (2022)
Collaboration Network
Top Collaborators
Mahanazuddin Syed
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
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
Melody Greer
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
Shorabuddin Syed
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
Joseph VanScoy
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
Sudeepa Bhattacharyya
Arkansas State University
1 shared publication
In database
- DeIDNER Model: A Neural Network Named Entity Recognition Model for Use in the De-identification of Clinical Notes
Meredith Zozus
The University of Texas at San Antonio Health Science Center (US)
1 shared publication
- DeIDNER Model: A Neural Network Named Entity Recognition Model for Use in the De-identification of Clinical Notes
Fred Prior
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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