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Ang Li: New AI Model Streamlines VTE Detection from Full Clinical Notes
Ang Li/Ang Li Lab Baylor College of Medicine
Aug 8, 2025, 13:39

Ang Li: New AI Model Streamlines VTE Detection from Full Clinical Notes

Ang Li, Assistant Professor of Hematology & Oncology, Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, shared an exciting post on X:

”Tired of spending all day chart-reviewing to find acute venous thrombosis (PE/DVT)?
Try our validated VTE-BERT, a fine-tuned NLP AI model that extracts VTE outcomes from full clinical notes.”

Try the interactive demo.

Read the full article in the Journal of Thrombosis and Haemostasis.

Learn more, visit Ang Li Lab.

Title: Development and Validation of VTE-BERT Natural Language Processing Model for Venous Thromboembolism

Authors: Omid Jafari, Shengling Ma, Barbara D. Lam, Jun Y. Jiang, Emily Zhou, Mrinal Ranjan, Justine Ryu, Raka Bandyo, Arash Maghsoudi, Bo Peng, Christopher I. Amos, Abiodun Oluyomi, Nathanael R. Fillmore, Jennifer La, Ang Li

AI

Latest scientific advancements in hematology featured in Hemostasis Today.

Abiodun Oluyomi AI Ang Li Arash Maghsoudi Barbara D. Lam Bo Peng Christopher I. Amos Emily Zhou Health Hematology Hemostasis Today Jennifer La Journal of Thrombosis and Haemostasis JTH Jun Y. Jiang Justine Ryu Medicine Mrinal Ranjan Nathanael R. Fillmore NLP AI Omid Jafari PE Raka Bandyo Shengling Ma thrombosis VTE Detection VTE-BERT
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