Farhan Kawsar
Researcher
Also affiliated: Rice University (2019)
Unknown Researcher
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Biography and Research Information
OverviewAI-generated summary
Farhan Kawsar's research has focused on the application of machine learning techniques to medical data. He developed the DeIDNER model, a neural network designed for named entity recognition to facilitate the de-identification of clinical notes. This work addresses challenges in medical data privacy and usability. Kawsar also contributed to research exploring the validity of mutations as biological markers for tracking cancer progression over time. His scholarly output includes three publications, with a total of 71 citations and an h-index of 2. He has collaborated with several researchers at the University of Arkansas for Medical Sciences, including Jim Zhongning Chen, Ahmad Mazen Safar, Fred Prior, and Kevin W. Sexton, with whom he shares co-authored publications.
Metrics
- h-index: 2
- Publications: 3
- Citations: 72
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
- DeIDNER Model: A Neural Network Named Entity Recognition Model for Use in the De-identification of Clinical Notes
- DeIDNER Model: A Neural Network Named Entity Recognition Model for Use in the De-identification of Clinical Notes
- DeIDNER Model: A Neural Network Named Entity Recognition Model for Use in the De-identification of Clinical Notes
- DeIDNER Model: A Neural Network Named Entity Recognition Model for Use in the De-identification of Clinical Notes
- DeIDNER Model: A Neural Network Named Entity Recognition Model for Use in the De-identification of Clinical Notes
- DeIDNER Model: A Neural Network Named Entity Recognition Model for Use in the De-identification of Clinical Notes
- DeIDNER Model: A Neural Network Named Entity Recognition Model for Use in the De-identification of Clinical Notes
- DeIDNER Model: A Neural Network Named Entity Recognition Model for Use in the De-identification of Clinical Notes
- DeIDNER Model: A Neural Network Named Entity Recognition Model for Use in the De-identification of Clinical Notes
- DeIDNER Model: A Neural Network Named Entity Recognition Model for Use in the De-identification of Clinical Notes
- DeIDNER Model: A Neural Network Named Entity Recognition Model for Use in the De-identification of Clinical Notes
- Validity of mutations as a cancer chronometer.
- Validity of mutations as a cancer chronometer.
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