5 Dossier-Graph: Model Fine-Tuning for IQWiG Domain Knowledge
5.1 Overview
This guide provides comprehensive instructions for implementing the Dossier-Graph system, which creates a specialized AI assistant for Health Technology Assessment (HTA) tasks. The system fine-tunes large language models to understand and generate content following IQWiG (Institute for Quality and Efficiency in Health Care) standards.
5.1.1 System Requirements
- Operating System: Ubuntu 24 LTS (recommended)
- Memory: Minimum 16GB RAM
- GPU: NVIDIA GPU with 8-12GB VRAM
- Storage: 50GB available space
- Python: Version 3.8 or higher
5.1.2 Core Capabilities
The fine-tuned model will be helpful in assisting to:
- Generate HTA summaries following IQWiG methodology standards
- Evaluate evidence quality using IQWiG terminology
- Draft benefit assessments with appropriate regulatory language
- Analyze cost-effectiveness in accessible terms
- Provide methodological guidance based on IQWiG precedents
- Process both German and English HTA content
5.2 Implementation Architecture
5.2.1 Technical Stack
- Base Model: Mistral-7B-v0.1 (7 billion parameters)
- Fine-tuning Method: LoRA (Low-Rank Adaptation)
- Quantization: 4-bit precision for memory efficiency
- Framework: Hugging Face Transformers with PEFT
5.2.2 LoRA Configuration
target_modules = [
"q_proj", "k_proj", "v_proj", "o_proj", # Attention layers
"gate_proj", "up_proj", "down_proj", # MLP layers
"lm_head" # Output projection
]
lora_config = {
"r": 64, # Rank for medical terminology complexity
"alpha": 128, # 2x rank ratio for training stability
"dropout": 0.05, # Lower dropout for specialized domain
"bias": "none" # Standard for causal language models
}5.3 Data Preparation Pipeline
5.3.1 Step 1: Document Processing
Convert IQWiG reports into structured training data:
def process_hta_report(report_path):
"""Extract and structure content from IQWiG reports."""
# Extract text from PDF
text_content = extract_text_from_pdf(report_path)
# Create instruction-based training examples
training_example = {
"instruction": "Summarize the benefit assessment findings",
"input": f"IQWiG Report {report_id}: {text_content}",
"output": "The assessment concluded..."
}
return format_as_jsonl(training_example)5.3.2 Step 2: Training Data Structure
Each training example follows this JSONL format:
{
"instruction": "Evaluate the evidence quality for pembrolizumab in melanoma",
"input": "Single RCT, n=834 patients, 22-month follow-up, primary endpoint: PFS",
"output": "The evidence quality is rated as moderate (GRADE). While the single RCT provides robust data with adequate sample size and follow-up duration, the limitation to a single study reduces confidence in the generalizability of findings."
}5.4 Model Training Process
5.4.1 Step 1: Environment Setup
# Install required packages
pip install torch transformers peft trl datasets accelerate bitsandbytes
# Load and compress base model
from transformers import AutoModelForCausalLM, BitsAndBytesConfig
quantization_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=torch.float16,
bnb_4bit_quant_type="nf4"
)
model = AutoModelForCausalLM.from_pretrained(
"mistralai/Mistral-7B-v0.1",
quantization_config=quantization_config,
device_map="auto"
)5.4.2 Step 2: LoRA Application
The LoRA method adds small, trainable matrices to specific model layers:
5.4.3 Step 3: Training Loop
training_args = TrainingArguments(
num_train_epochs=3,
per_device_train_batch_size=4,
gradient_accumulation_steps=4,
learning_rate=2e-4,
warmup_steps=100,
logging_steps=25,
save_strategy="epoch",
evaluation_strategy="steps",
eval_steps=100
)
trainer = SFTTrainer(
model=model,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
args=training_args,
max_seq_length=2048
)
trainer.train()5.5 Model Validation
5.5.1 Evaluation Metrics
5.5.1.1 1. Perplexity
What it measures: How “surprised” the model is by unseen HTA text - lower scores mean better prediction.
Rationale: Perplexity quantifies whether the model has genuinely learned HTA language patterns. If a model achieves low perplexity on held-out IQWiG reports, it demonstrates it can predict the next word in HTA contexts accurately. For example, after “The evidence quality is rated as”, a well-trained model should predict “moderate” or “high” (low perplexity), not random medical terms (high perplexity).
Why essential: It’s an objective, automated metric that catches overfitting - if perplexity is low on training data but high on validation data, the model memorized rather than learned.
5.5.1.2 2. ROUGE Scores
What it measures: Overlap between generated summaries and expert-written reference summaries (word/phrase matching).
Rationale: HTA reports require precise summarization of complex evidence. ROUGE scores (Recall-Oriented Understudy for Gisting Evaluation) compare the model’s summaries against gold-standard IQWiG summaries, measuring whether key phrases like “considerable additional benefit” or “hint of lesser benefit” appear correctly. ROUGE-L specifically captures longest common sequences, ensuring the model maintains IQWiG’s logical flow.
Why essential: Validates that the model can distill lengthy clinical evidence into concise, accurate assessments matching IQWiG’s standardized summary style.
5.5.1.3 3. Domain-Specific Accuracy
What it measures: Correct usage of IQWiG-specific terminology and methodological concepts.
Rationale: IQWiG uses precise regulatory language with specific meanings. The model must distinguish between “proof” vs “indication” vs “hint” of benefit (Beleg/Hinweis/Anhaltspunkt in German), use GRADE terminology correctly, and apply the right benefit categories. This metric tests terminology on a curated test set - for instance, given specific evidence, does the model correctly classify it as “major” vs “considerable” additional benefit?
Why essential: Generic medical language isn’t sufficient for regulatory submissions. Misusing terms like “significant benefit” instead of IQWiG’s exact categories could invalidate an assessment.
5.5.1.4 4. Human Expert Review
What it measures: Qualitative assessment of readability, logical coherence, and regulatory appropriateness.
Rationale: Automated metrics miss critical nuances that HTA professionals immediately recognize. Experts evaluate whether the model’s outputs would be acceptable in actual IQWiG proceedings - checking for logical argumentation, appropriate hedging language, correct interpretation of statistical significance vs clinical relevance, and adherence to IQWiG’s methodological standards that aren’t captured by word matching.
Why essential: The ultimate test is whether HTA professionals would trust and use the output. Expert review catches dangerous errors (like misinterpreting non-inferiority trials) that automated metrics might miss.
5.5.1.5 Combined Rationale
These four metrics create a comprehensive evaluation framework:
- Perplexity ensures fundamental language modeling capability
- ROUGE validates content accuracy and completeness
- Domain Accuracy confirms regulatory compliance
- Expert Review provides the final quality gate
Together, they prevent deploying a model that seems statistically competent but fails in real HTA contexts - a critical safety requirement for regulatory healthcare applications.
5.5.2 Validation Strategy
# Split data: 80% training, 20% validation
train_reports = reports[:int(0.8 * len(reports))]
val_reports = reports[int(0.8 * len(reports)):]
# Test on unseen reports
validation_results = model.evaluate(val_reports)
if validation_results['perplexity'] > threshold:
# Consider increasing training data or adjusting hyperparameters
optimize_training_parameters()5.6 Deployment and Inference
5.6.1 Model Usage
def generate_hta_response(question, context):
"""Generate HTA-compliant responses."""
# Format input following training schema
prompt = f"[INST] {question}\n\nContext: {context} [/INST]"
# Tokenize and generate
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(
inputs.input_ids,
max_new_tokens=512,
temperature=0.7,
do_sample=True,
top_p=0.95
)
return tokenizer.decode(outputs[0], skip_special_tokens=True)5.6.2 Example Applications
# Evidence quality assessment
response = generate_hta_response(
question="Assess the evidence quality for this intervention",
context="Two RCTs (n=1,245 total), 12-month follow-up, consistent findings"
)
# Benefit summary generation
response = generate_hta_response(
question="Summarize the additional benefit assessment",
context="IQWiG Report A23-42: Primary endpoint met, QoL improved..."
)5.7 Critical Success Factors
5.7.1 Data Quality
- Consistency: Ensure uniform terminology across training examples
- Completeness: Include all relevant IQWiG assessment types
- Accuracy: Verify expert annotations before training
5.8 Mathematical Foundation
5.8.1 LoRA Mechanism
Instead of updating all model parameters, LoRA introduces low-rank decomposition:
\[W_{new} = W_{original} + BA\]
Where: - \(W_{original}\) ∈ ℝ^{d×k} (frozen pre-trained weights) - \(B\) ∈ ℝ^{d×r} and \(A\) ∈ ℝ^{r×k} (trainable matrices) - \(r << min(d, k)\) (rank constraint)
This reduces trainable parameters from O(dk) to O(r(d+k)), typically a 99.7% reduction.
5.9 Installation Guide
5.9.1 Quick Start
Do not run: work in progress!
# Clone repository
git clone https://github.com/your-org/dossier-graph.git
cd dossier-graph
# Create virtual environment
python -m venv venv
source venv/bin/activate
# Install dependencies
pip install -r requirements.txt
# Run training pipeline
python train_mistral_lora_hta.py \
--data_path ./data/iqwig_reports \
--output_dir ./models/hta_mistral \
--num_epochs 35.9.2 Production Deployment
For production environments, consider:
- API Service: Deploy model behind REST API for scalability
- Batch Processing: Implement queue system for high-volume requests
- Monitoring: Track inference latency and model performance metrics
- Version Control: Maintain model versioning for reproducibility
5.10 Troubleshooting
5.10.1 Common Issues
| Issue | Cause | Solution |
|---|---|---|
| Out of Memory | Large batch size | Reduce batch size or increase gradient accumulation |
| Poor Performance | Insufficient data | Increase training examples or use data augmentation |
| Overfitting | Too many epochs | Implement early stopping based on validation loss |
| Slow Training | Inefficient settings | Enable mixed precision training (fp16) |
5.11 Conclusion
The Dossier-Graph system enables organizations to create specialized AI assistants that understand and generate HTA content following IQWiG standards. By leveraging LoRA fine-tuning on carefully curated datasets, the system achieves domain expertise while maintaining computational efficiency.
5.12 References
- Hu, E. J., et al. (2021). LoRA: Low-Rank Adaptation of Large Language Models. arXiv:2106.09685
- IQWiG Methods Papers and Guidelines: www.iqwig.de
- Hugging Face PEFT Documentation: https://huggingface.co/docs/peft
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