The development of this website has started in the Spring of 2024 by Omid Jafari as part of the grant described below.

The goal of this website is to provide an end-to-end platform for medical professionals to help them annotate their data and train Natural Language Processing (NLP) models without requiring technical knowledge.

The trained models can later be used to assist in the annotation process and provide clinical decision support.

This is a work in progress, and more components will be added to the website.

The grant supporting this website can be found below:

• Title of the awarded project: Utilizing ethical artificial intelligence/machine learning (AI/ML) to assess and address disparity in underserved cancer patients across multiple healthcare systems with diverse social determinants of health
• Project PI: Abiodun Oluyomi
• Project Co PI: Ang Li, Jennifer La
• Funding Opportunity: 3OT2OD032581-01S1

Resources:

• VTE-BERT Model: Our fine-tuned model optimized for VTE classification is available under gated access on Hugging Face.
• GitHub: NLPMed-Engine, a robust and extensible NLP engine tailored for medical text, internally using VTE-BERT.
• Publications:
  1. Jafari, O., Ma, S., Lam, B. D., Jiang, J. Y., Zhou, E., Ranjan, M., ... & Li, A. Development and Validation of VTE-BERT Natural Language Processing Model for Venous Thromboembolism. Journal of Thrombosis and Haemostasis. DOI: 10.1016/j.jtha.2025.07.021 (Open Access)