Omid Jafari and Colleagues Present the VTE-BERT Natural Language Processing Model
Journal of Thrombosis and Haemostasis (JTH) shared on LinkedIn:
”Development and validation of venous thromboembolism–Bidirectional Encoder Representations from Transformers (VTE-BERT) natural language processing model
By Omid Jafari, Shengling M., Barbara Lam, Jun Y. Jiang, Emily Zhou, Mrinal Ranjan, Justine Ryu, Raka Bandyo, Arash Maghsoudi, Bo Peng, Christopher Amos, Abiodun Oluyomi, Nathanael (Nate) Fillmore, Jennifer La, and Ang Li
• Concept: Accurate phenotyping of venous thromboembolism (VTE) in longitudinal datasets remains labor-intensive; an NLP model could automate detection.
• Approach: Using >28 000 annotated clinical notes from two institutions, the team fine-tuned Bio_Clinical BERT to create VTE-BERT, trained to recognize acute VTE events and anatomic sites over time.
• Findings: Achieved 95% precision / 98% recall internally and 85% / 92% externally, outperforming manual chart review efficiency. This tool could markedly accelerate large-scale thrombosis research.”
Read the full article here.
Article: Development and validation of venous thromboembolism–bidirectional encoder representations from transformers (VTE-BERT) natural language processing model
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
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