---
language: en
license: apache-2.0
tags:
- medical
- clinical-notes
- drug-detection
- substance-use
- multi-task-learning
- healthcare-nlp
- lora
- peft
base_model: mistralai/Ministral-3-14B-Instruct-2512
datasets:
- custom
metrics:
- f1
- precision
- recall
pipeline_tag: text-generation
---

# Ministral-DrugDetector-14B-MFI

## Model Description

**Ministral-DrugDetector-14B-MFI** is a LoRA fine-tuned model for detecting illicit drug use in clinical notes. It is based on [mistralai/Ministral-3-14B-Instruct-2512](https://huggingface.co/mistralai/Ministral-3-14B-Instruct-2512) (14B parameters).

The model performs **multi-task classification**, predicting:
1. **Illicit use detection** for Methamphetamine, Fentanyl, and Injection Drug Use (True/False/Unknown)
2. **Temporal classification** for each drug (Current/Historical/Unknown/N/A)

## Performance

**Ranked 1st out of 3 models** in our evaluation (Macro F1: 0.940)

### Illicit Use Detection (F1 Scores)

| Drug | F1 | Precision | Recall |
|------|-----|-----------|--------|
| Methamphetamine | 0.931 | 0.904 | 0.959 |
| Fentanyl | 0.969 | 1.000 | 0.939 |
| Injection Drug Use | 0.921 | 0.967 | 0.879 |
| **Macro Average** | **0.940** | | |

### Temporal Classification (Current vs Historical)

| Drug | Accuracy |
|------|----------|
| Methamphetamine | 0.723 |
| Fentanyl | 0.710 |
| Injection Drug Use | 0.679 |
| **Average** | **0.704** |

### Model Comparison

| Model | Size | Macro F1 | Temporal Acc |
|-------|------|----------|--------------|
| Ministral-DrugDetector-14B-MFI | 14B | **0.940** | **0.704** |
| Llama-DrugDetector-8B-MFI-v2 | 8B | 0.876 | 0.533 |
| MediPhi-DrugDetector-MFI | 3.8B | 0.863 | 0.502 |

## Usage

```python
from transformers import Mistral3ForConditionalGeneration, MistralCommonBackend
from peft import PeftModel
import torch

# Load base model
base_model = "mistralai/Ministral-3-14B-Instruct-2512"
model = Mistral3ForConditionalGeneration.from_pretrained(
    base_model,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Load LoRA adapter
model = PeftModel.from_pretrained(model, "fabriceyhc/Ministral-DrugDetector-14B-MFI")

# Load tokenizer
tokenizer = MistralCommonBackend.from_pretrained("fabriceyhc/Ministral-DrugDetector-14B-MFI")

# Task instruction
task_instruction = """### 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**

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

**Temporal Classification:**
- **Current**: Present tense, recent use, positive test
- **Historical**: Past tense, "history of", "former user"
- **Unknown**: Timeframe unclear

### 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>"""

# Example note
note_text = "Patient reports using meth daily for the past 2 weeks. Denies IV drug use."

# Format prompt
prompt = f"""<s>[INST] {task_instruction}

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

### Answer: [/INST]"""

# 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 Details

- **Base Model**: [mistralai/Ministral-3-14B-Instruct-2512](https://huggingface.co/mistralai/Ministral-3-14B-Instruct-2512)
- **Fine-tuning Method**: LoRA (Low-Rank Adaptation)
- **LoRA Rank**: 16
- **LoRA Alpha**: 32
- **Training Data**: 93 annotated clinical note samples
- **Validation Data**: 21 samples
- **Test Data**: 129 samples
- **Epochs**: 5 (with early stopping)
- **Learning Rate**: 2e-4
- **Precision**: bfloat16

## Limitations

1. **Domain-specific**: Trained on clinical notes from specific healthcare systems
2. **Drug Coverage**: Limited to methamphetamine, fentanyl, and injection drug use
3. **Context Window**: May truncate very long notes
4. **Temporal Accuracy**: ~50-70% accuracy on Current vs Historical classification

## Intended Use

**Recommended:**
- Retrospective chart review for substance use research
- Clinical decision support (with human review)
- Cohort identification for substance use studies

**Not Recommended:**
- Fully automated clinical decision-making without human oversight
- Forensic or legal determination of substance use

## Citation

```bibtex
@misc{ministral-drugdetector-14b-mfi-2025,
  author = {Hanna-Chang, Fabrice},
  title = {Ministral-DrugDetector-14B-MFI},
  year = {2025},
  publisher = {HuggingFace},
  url = {https://huggingface.co/fabriceyhc/Ministral-DrugDetector-14B-MFI}
}
```

## License

APACHE-2.0