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---
license: mit
base_model:
- microsoft/Phi-3-mini-4k-instruct
tags:
- Medical
- MedicalCoding
- Pharma
---
# 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. |