Draft Status
Ready - team will start page creating immediately
Category
Infrastructure
Key Investigators
- Paul Dumont (University of North Carolina at Chapel Hill, USA)
- Alexandre Buisson (University of North Carolina at Chapel Hill, USA)
- Juan Carlos Prieto (University of North Carolina at Chapel Hill, USA)
- Eduardo Caleme (University of North Carolina at Chapel Hill, USA)
- Lucia Cevidanes (University of North Carolina at Chapel Hill, USA)
- Steve Pieper (Isomics, USA)
Project Description
This project develops a privacy-preserving extraction framework using locally deployable open-weight LLMs to structure dense clinical narratives. We aim to bypass expensive and non-private cloud APIs by fine-tuning models to extract Common Data Elements (CDEs) from Orthodontic and TMJ progress notes entirely offline.
Objective
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Fine-tune local LLMs (Llama-3.1-8B, Qwen-2.5-7B) to parse variable clinical shorthand into deterministic JSON dictionaries.
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Benchmark these local models against cloud baselines and human inter-rater ceilings for accuracy and computational efficiency.
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Deploy these trained models directly into 3D Slicer by developing a custom extension.
Approach and Plan
-
Fine-tune Meta-Llama-3.1-8B for orthodontic notes and Qwen-2.5-7B for TMJ records.
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Evaluate the extractions using exact match F1-scores.
-
Build a fully offline 3D Slicer extension for secure, on-device data processing.
Progress and Next Steps
Fine-tuned Llama-3.1-8B (LoRA) achieved a 0.740 F1 score on orthodontic notes , and fully fine-tuned Qwen-2.5-7B reached a 0.78 F1 score on TMJ records.
Next Steps: Deploy these trained models directly within 3D Slicer by developing a custom extension with a dedicated user interface (UI) for clinical application.
Illustrations
An illustrative example of the pipeline for local clinical data extraction.
Background and References
No response
Draft Status
Ready - team will start page creating immediately
Category
Infrastructure
Key Investigators
Project Description
This project develops a privacy-preserving extraction framework using locally deployable open-weight LLMs to structure dense clinical narratives. We aim to bypass expensive and non-private cloud APIs by fine-tuning models to extract Common Data Elements (CDEs) from Orthodontic and TMJ progress notes entirely offline.
Objective
Fine-tune local LLMs (Llama-3.1-8B, Qwen-2.5-7B) to parse variable clinical shorthand into deterministic JSON dictionaries.
Benchmark these local models against cloud baselines and human inter-rater ceilings for accuracy and computational efficiency.
Deploy these trained models directly into 3D Slicer by developing a custom extension.
Approach and Plan
Fine-tune Meta-Llama-3.1-8B for orthodontic notes and Qwen-2.5-7B for TMJ records.
Evaluate the extractions using exact match F1-scores.
Build a fully offline 3D Slicer extension for secure, on-device data processing.
Progress and Next Steps
Fine-tuned Llama-3.1-8B (LoRA) achieved a 0.740 F1 score on orthodontic notes , and fully fine-tuned Qwen-2.5-7B reached a 0.78 F1 score on TMJ records.
Next Steps: Deploy these trained models directly within 3D Slicer by developing a custom extension with a dedicated user interface (UI) for clinical application.
Illustrations
An illustrative example of the pipeline for local clinical data extraction.
Background and References
No response