ICD-10 Code Predictor
A fine-tuned language model that predicts ICD-10 diagnosis codes from clinical text descriptions.
Model Details
Model Description
This model takes a plain English description of a patient's symptoms or condition and outputs the corresponding ICD-10 diagnosis code. It is built on Meta's Llama 3.2 3B base model, fine-tuned using LoRA (Low-Rank Adaptation) with the Unsloth library for efficient training.
- Developed by: StudioIlios
- Model type: Causal Language Model (LoRA fine-tuned)
- Language(s): English (clinical/medical text)
- Base Model: meta-llama/Llama-3.2-3B
- Fine-tuning method: LoRA via Unsloth
- License: [More Information Needed]
Uses
Direct Use
Input a clinical description of a patient's condition and the model will return the predicted ICD-10 code.
Example prompt:
Patient has diabetes mellitus with high blood sugar. What is the ICD10 code?
Example output:
The ICD10 code for Diabetes mellitus is E11.9
Downstream Use
- Medical billing automation
- Insurance claim processing
- EHR (Electronic Health Record) systems
- Healthcare apps requiring automatic diagnosis code suggestion
Out-of-Scope Use
- This model should not be used as a substitute for professional medical diagnosis
- Not suitable for rare or highly complex conditions without human verification
- Not intended for real-time critical care decisions
Bias, Risks, and Limitations
- Model predictions should always be verified by a qualified medical coder or physician
- May not accurately predict codes for uncommon or highly specific conditions
- Performance depends on how clearly the condition is described in the input
Recommendations
Always have a medical professional review the predicted ICD-10 codes before using them for billing or insurance purposes.
How to Get Started with the Model
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-3B")
tokenizer = AutoTokenizer.from_pretrained("StudioIlios/icd10-model")
model = PeftModel.from_pretrained(base_model, "StudioIlios/icd10-model")
prompt = """
Patient has diabetes mellitus with high blood sugar.
What is the ICD10 code?
"""
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=100, temperature=0.7, do_sample=True)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Training Details
Training Data
Fine-tuned on medical clinical text paired with ICD-10 diagnosis codes.
Training Procedure
Training Hyperparameters
- Training regime: LoRA fine-tuning with bf16 mixed precision
- Library: Unsloth
- Base model: Llama 3.2 3B
Evaluation
Results
The model correctly predicts common ICD-10 codes from plain English clinical descriptions.
Sample tested:
| Input | Predicted Code |
|---|---|
| Diabetes mellitus with high blood sugar | E11.9 |
Technical Specifications
Model Architecture
- Base: Llama 3.2 3B (causal language model)
- Adapter: LoRA (Low-Rank Adaptation)
- Files:
adapter_config.json,adapter_model.safetensors
Hardware Used for Training
- GPU: NVIDIA Tesla T4 (Google Colab)