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.frompretrained("Kavyaah/medical-coding-llm") model = AutoModelForCausalLM.frompretrained("Kavyaah/medical-coding-llm") model.eval()

# Function to predict ICD/CPT codes def getcode(statement, maxnewtokens=50): prompt = f"Assign the correct ICD or CPT medical code for this case:\n{statement}\nCode:" inputs = tokenizer(prompt, returntensors="pt") with torch.nograd(): outputs = model.generate(**inputs, maxnewtokens=maxnewtokens, dosample=False) result = tokenizer.decode(outputs[0], skipspecialtokens=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.