medgemma-27b-medical-coding

medgemma-27b-medical-coding

Model Description

This is a LoRA adapter fine-tuned on medgemma27B for medical coding tasks. The model is specifically designed to:

• Extract diseases and medical conditions from discharge summaries
• Identify medical procedures and interventions
• Assign appropriate medical codes (ICD-10, CPT, HCPCS)
• Process clinical documentation with high accuracy

Base Model: medgemma27B
Fine-tuning Method: LoRA (Low-Rank Adaptation)

Training Details

LoRA Configuration

• Rank (r): 8
• Alpha: 16
• Dropout: 0.1
• Target Modules: q_proj, up_proj, v_proj, gate_proj

Usage

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

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("TachyHealthResearch/medgemma-27b-medical-coding")

# Load base model
base_model = AutoModelForCausalLM.from_pretrained(
    "medgemma27B",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True
)

# Load LoRA adapter
model = PeftModel.from_pretrained(base_model, "TachyHealthResearch/medgemma-27b-medical-coding")

Example Usage

# Define the system prompt for medical coding
system_prompt = """You are an expert medical coding specialist. 
Analyze the discharge summary to extract diseases, procedures, and assign appropriate medical codes. 
Return the response in JSON format with this structure:
{"diseases": ["disease1", "disease2"], "icd10_codes": ["code1", "code2"], 
"procedures": ["procedure1", "procedure2"], "cpt_codes": ["code1", "code2"], 
"hcpcs_codes": ["code1", "code2"]}"""

# Example discharge summary
discharge_summary = """
Patient admitted with chest pain and shortness of breath. 
Diagnosed with acute myocardial infarction and congestive heart failure.
Underwent percutaneous coronary intervention with stent placement.
"""

# Prepare the conversation
messages = [
    {"role": "system", "content": system_prompt},
    {"role": "user", "content": f"Please analyze this discharge summary:\n\n{discharge_summary}"}
]

# Apply chat template and generate
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=512,
        temperature=0.1,
        do_sample=True,
        pad_token_id=tokenizer.eos_token_id,
    )

response = tokenizer.decode(outputs[0], skip_special_tokens=True)
generated_response = response[len(text):].strip()
print("Generated Medical Codes:")
print(generated_response)

Model Performance

This model has been specifically fine-tuned for medical coding tasks and demonstrates strong performance in:

• Disease extraction from clinical text
• Medical procedure identification
• Medical code assignment (ICD-10, CPT, HCPCS)
• Structured JSON response generation

Intended Use

Primary Use Cases

• Medical coding automation
• Clinical documentation analysis
• Healthcare data processing

Limitations

• Always verify generated codes with qualified medical coding professionals
• Performance may vary on clinical documents significantly different from training data
• Intended for use in appropriate healthcare environments only

License

This model is released under the Apache 2.0 License.

Citation

If you use this model in your research or applications, please cite:

@misc{TachyHealthResearch_medgemma_27b_medical_coding_2024},
  title = {medgemma-27b-medical-coding: Medical Coding Model},
  author = {TachyHealthResearch},
  year = {2024},
  publisher = {Hugging Face},
  url = {https://huggingface.co/TachyHealthResearch/medgemma-27b-medical-coding}
}

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Important: This model is intended for research and healthcare applications. Always ensure proper validation and human oversight when using AI models in medical contexts.