Llama-DrugDetector-8B-MFI

Llama-DrugDetector-8B-MFI

Model Description

Llama-DrugDetector-8B-MFI is a fine-tuned medical NLP model for detecting illicit drug use in clinical notes. It specializes in identifying:

• Methamphetamine (illicit use)
• Fentanyl (illicit use)
• Injection Drug Use (IDU/IVDU)

The model performs multi-task learning, simultaneously predicting:

1. Illicit use detection (True/False/Unknown)
2. Temporal classification (Current/Historical/Unknown/N/A)

Model Architecture

• Base Model: Meta Llama 3.1 8B
• Fine-tuning: LoRA (Low-Rank Adaptation)
  • Rank: 16
  • Alpha: 32
  • Target modules: q_proj, v_proj, k_proj, o_proj
• Training Method: Multi-task instruction following
• Context Length: 2048 tokens

Performance

Evaluated on 159 clinical note sentences:

Comparison with Base Model

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
import torch

# Load base model
base_model = "fabriceyhc/Llama-DrugDetector-8B"
model = AutoModelForCausalLM.from_pretrained(
    base_model,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Load LoRA adapter
model = PeftModel.from_pretrained(model, "fabriceyhc/Llama-DrugDetector-8B-MFI")

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("fabriceyhc/Llama-DrugDetector-8B-MFI")

# Create prompt
note_text = "Patient reports using meth daily for the past 2 weeks."

prompt = f"""### Task Description:
Please carefully review the following medical note and identify illicit drug use.

**CRITICAL RULES:**
1. **Positive drug test → ALWAYS ILLICIT** (unless in medication list)
2. **PMH/History of use → ILLICIT** (even if historical)
3. **Substance use disorder → ILLICIT**
4. **Patient self-reports (endorses, reports, admits) → ILLICIT**
5. **Prescribed/medical use → NOT ILLICIT**:
   - Medication lists with dosages
   - "Given", "administered" in medical context
   - Pain control, procedural use (fentanyl)
   - Prescription quantities

**Drugs to identify:**
- **Methamphetamine**: Illicit amphetamine use (not prescribed Adderall for ADHD)
- **Fentanyl**: Illicit fentanyl use (not prescribed patches/procedural use)
- **Injection Drug Use**: IV drug use (IVDU, IVDA), including IV heroin, cocaine, meth

**Temporal Classification** (for illicit cases only):
- **Current**: Present tense, recent use, positive test results, "POA" (present on arrival)
- **Historical**: Past tense, "history of", "former user", "in remission"
- **Unknown**: Timeframe unclear or ambiguous

### Desired Format:

Methamphetamine Illicit Use: <True/False/Unknown>
Fentanyl Illicit Use: <True/False/Unknown>
Injection Drug Use: <True/False/Unknown>
Methamphetamine Temporal Status: <Current/Historical/Unknown/N/A>
Fentanyl Temporal Status: <Current/Historical/Unknown/N/A>
Injection Drug Use Temporal Status: <Current/Historical/Unknown/N/A>

### The medical note to evaluate:
{note_text}

### Answer:
"""

# Generate
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(
    **inputs,
    max_new_tokens=200,
    do_sample=False,
    pad_token_id=tokenizer.eos_token_id
)

# Decode
result = tokenizer.decode(outputs[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True)
print(result)

Expected Output

Methamphetamine Illicit Use: True
Fentanyl Illicit Use: False
Injection Drug Use: False
Methamphetamine Temporal Status: Current
Fentanyl Temporal Status: N/A
Injection Drug Use Temporal Status: N/A

Training Data

• Training Set: 113 annotated clinical note examples
• Validation Set: 23 examples
• Test Set: 23 examples (separate from evaluation above)

Data includes:

• Discharge summaries
• H&P (History & Physical) notes
• Progress notes
• Emergency department notes

Annotations cover diverse clinical scenarios:

• Positive drug tests (urine, blood)
• Patient self-reports
• Substance use disorder diagnoses
• Prescribed medications (negative examples)
• Medical procedures using fentanyl (negative examples)
• Historical vs current use

Training Details

• Epochs: 5
• Best Checkpoint: Epoch 3.98
• Best Validation Loss: 0.740
• Batch Size: 2 per device × 2 GPUs
• Gradient Accumulation: 2 steps
• Learning Rate: 2e-4
• Optimizer: AdamW with warmup
• Gradient Checkpointing: Enabled
• Mixed Precision: bfloat16

Limitations

1. Recall Trade-off: Achieves 94.7% (meth) and 97.4% (fentanyl) recall, missing some illicit cases compared to perfect recall of base model
2. Domain-specific: Trained on specific clinical note formats and may not generalize to all medical documentation styles
3. Drug Coverage: Limited to methamphetamine, fentanyl, and injection drug use. Does not detect other substances (cocaine, opioids, cannabis, etc.)
4. Context Window: 2048 tokens may truncate very long notes
5. Unknown Predictions: May output "Unknown" for ambiguous cases (e.g., drug test ordered but results not available)

Intended Use

Primary Use Cases:

• Retrospective chart review for substance use research
• Clinical decision support (with human review)
• Population health screening
• Cohort identification for substance use studies

NOT Recommended For:

• Fully automated clinical decision-making
• Forensic or legal determination of substance use
• Real-time clinical alerts without human oversight

Ethical Considerations

• Bias: Model trained on data from specific healthcare systems; performance may vary across populations
• Privacy: Ensure proper de-identification before processing clinical notes
• False Negatives: Missing 2.6-5.3% of illicit cases could impact clinical outcomes
• Stigma: Use predictions responsibly; substance use disorder is a medical condition requiring compassionate care

Citation

@misc{llama-drugdetector-mfi-2025,
  author = {Harel-Canada, Fabrice},
  title = {Llama-DrugDetector-8B-MFI: Multi-task Clinical Drug Detection},
  year = {2025},
  publisher = {HuggingFace},
  url = {https://huggingface.co/fabriceyhc/Llama-DrugDetector-8B-MFI}
}

License

Llama 3.1 Community License Agreement

Contact

For questions or issues, please open an issue on the model repository.