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Biography and Research Information
OverviewAI-generated summary
Farhan Kawsar's research focuses on the application of advanced computational techniques to analyze complex biological and clinical data. His work includes the development of the DeIDNER model, a neural network designed for named entity recognition to facilitate the de-identification of clinical notes. This model contributes to privacy preservation in medical data processing. Kawsar also investigates the potential of mutations as a measure of cancer progression, exploring biological markers for disease timing. His scholarly output, while modest in volume, is active, with recent publications in 2022. Kawsar collaborates with researchers at the University of Arkansas for Medical Sciences, including Jim Zhongning Chen, Ahmad Mazen Safar, Melody Greer, and Fred Prior, contributing to a shared body of work in the medical research field.
Metrics
- h-index: 2
- Publications: 3
- Citations: 70
Selected Publications
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Validity of mutations as a cancer chronometer. (2022)
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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 (US)
1 shared publication
- 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
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
Sudeepa Bhattacharyya
Arkansas Biosciences Institute
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 Health Science Center at San Antonio (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
Ahmad Mazen Safar
University of Arkansas for Medical Sciences
1 shared publication
In database
- Validity of mutations as a cancer chronometer.
Jim Zhongning Chen
University of Arkansas for Medical Sciences
1 shared publication
In database
- Validity of mutations as a cancer chronometer.
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