Kavyaah/medical-coding-llm

Medical Coding LLM

Predict ICD-10 and CPT codes from clinical notes using a fine-tuned LLM.

This model is fine-tuned on clinical notes using Phi-3-mini with LoRA and 4-bit quantization. It can generate both ICD/CPT codes and short explanations, helping automate the medical coding process.

Model Details

Base Model: microsoft/Phi-3-mini-4k-instruct

Fine-Tuning: LoRA (r=16, alpha=32, dropout=0.05)

Quantization: 4-bit (BitsAndBytes NF4)

Training Dataset: Custom dataset of clinical notes, ICD codes, and supporting evidence

Task: Causal Language Modeling for code prediction

Usage

#
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch, re

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("Kavyaah/medical-coding-llm")
model = AutoModelForCausalLM.from_pretrained("Kavyaah/medical-coding-llm")
model.eval()

# Function to predict ICD/CPT codes
def get_code(statement, max_new_tokens=50):
  prompt = f"Assign the correct ICD or CPT medical code for this case:\n{statement}\nCode:"
  inputs = tokenizer(prompt, return_tensors="pt")
  with torch.no_grad():
    outputs = model.generate(**inputs, max_new_tokens=max_new_tokens, do_sample=False)
    result = tokenizer.decode(outputs[0], skip_special_tokens=True)

# Extract code using regex
if "Code:" in result:
    result = result.split("Code:")[-1]
match = re.search(r"\b[A-Z]\d{1,3}\.?[A-Z0-9]*\b", result)
return match.group(0).strip() if match else result.strip()

# Example
statement = "Patient diagnosed with Type 2 diabetes mellitus without complications."
print(get_code(statement))
# Output: E11.9

Evaluation

Exact match accuracy: 25%

Semantic accuracy (ICD block match): 50%

Intended Use

Assisting medical coders and healthcare professionals.

Automating initial code suggestions from clinical notes.

Limitations

Trained on a small dataset; may not cover all ICD/CPT codes.

Use as an assistive tool, not a replacement for professional judgment.

Always review predicted codes before clinical or billing use.

License

MIT License — feel free to use and adapt for non-commercial purposes.