Inquiry About Developing a NER Model for Medical Diagnosis

[KISHORE B AIML]

Dear all nltk-users,

I hope this message finds you well.
I am currently a member of the NLTK-Users community and am exploring ways to develop a Named Entity Recognition (NER) model for medical diagnosis. I am particularly interested in understanding the best practices and resources available for creating a model that can effectively extract and classify relevant medical entities(diagnosis)from extracted content via pdf.

Could you kindly provide guidance on how I can approach the development of such a model? Any insights on datasets, tools, or methodologies specifically suited for medical NER tasks would be greatly appreciated.
Additionally, if there are any workshops, documentation, or experts within the community who could assist me in this process, I would be grateful for any recommendations.

Thank you for your time and assistance. I look forward to your response.

[Mustafa Jarrar]

Dear Kishore,

We have an NER module called Wojood. You can explore the demo here: https://sina.birzeit.edu/wojood/.

In the Download section of the demo, you'll find:

- The published paper on Wojood

- The trained model available on Hugging Face

- The training code with sample data on GitHub

- The corpus itself for download

Additionally, Wojood can be used as part of SinaTools, an open-source toolkit for Arabic NLP that provides Python APIs, command-line tools, and more. You can find it here: https://sina.birzeit.edu/sinatools/.

Please feel free to reach out if you have any questions or need further assistance.

Best regards,

Mustafa

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