Model Card for AI-Driven Health Literacy Simplification Model
This model simplifies complex medical texts to a 6th-grade reading level, enhancing health literacy among patients with low health literacy.
Model Details
Model Description
This model uses advanced natural language processing (NLP) algorithms to translate complex medical information into a format that is accessible to individuals with a 6th-grade reading level. The goal is to improve comprehension and health outcomes for patients with low health literacy.
- Developed by: Wernicke AI
- Funded by: ME [More Information Needed]
- Shared by: [More Information Needed]
- Model type: Text Simplification
- Language(s) (NLP): English
- License: Creative Commons Attribution Non-Commercial 3.0
- Finetuned from model: tiiuae/falcon-40b
Uses
Direct Use
The model can be used directly to simplify patient education materials to improve accessibility and comprehension.
Downstream Use
The model can be integrated into healthcare platforms and patient portals to provide simplified information, aiding patients in understanding their medical conditions and treatment plans.
Out-of-Scope Use
The model should not be used for generating medical advice or instructions without proper validation from healthcare professionals to avoid misinformation.
Bias, Risks, and Limitations
The model may not fully capture all nuances of medical information, leading to oversimplification or loss of critical details. There is also a risk of bias in the training data affecting the output.
Recommendations
Users should validate the simplified text with healthcare professionals to ensure accuracy and completeness of the information.
How to Get Started with the Model
Use the code below to get started with the model.
from transformers import (
AutoConfig,
AutoModelForCausalLM,
AutoTokenizer,
BitsAndBytesConfig
)
from peft import PeftConfig
MODEL = "9rofe/Wernicke-AI3"
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.bfloat16
)
config = PeftConfig.from_pretrained(MODEL)
model = AutoModelForCausalLM.from_pretrained(
config.base_model_name_or_path,
return_dict=True,
quantization_config=bnb_config,
device_map="auto",
trust_remote_code=True
)
tokenizer=AutoTokenizer.from_pretrained(config.base_model_name_or_path)
tokenizer.pad_token = tokenizer.eos_token
model = PeftModel.from_pretrained(model, MODEL)
generation_config = model.generation_config
generation_config.max_new_tokens = 500 # MODIFY
generation_config.temperature = 0.7
generation_config.top_p = 0.7
generation_config.num_return_sequences = 1
generation_config.pad_token_id = tokenizer.eos_token_id
generation_config.eos_token_id = tokenizer.eos_token_id
%%time
device = "cuda:0"
prompt = """
<user>: Convert this text to reading level 6: {TEXT}
<assistant>:
""".strip()
encoding = tokenizer(prompt, return_tensors="pt").to(device)
with torch.inference_mode():
outputs = model.generate(
input_ids = encoding.input_ids,
attention_mask = encoding.attention_mask,
generation_config = generation_config
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Utilize this prompt:
prompt = """
<user>: Convert this text to reading level 6: {TEXT}
<assistant>:
""".strip()
Training Details
Training Data
The model was trained on a comprehensive dataset of medical texts, including patient handouts and educational materials, processed to ensure readability compliance with NIH and AMA guidelines.
Training Procedure
Preprocessing
Medical texts were preprocessed using readability assessments such as SMOG, Flesch-Kincaid, and Gunning Fog to ensure the dataset's appropriateness for training the simplification model.
Training Hyperparameters
- Training regime: Training regime: fp16 mixed precision Optimizer: AdamW Learning rate: 5e-5 Batch size: 32
Speeds, Sizes, Times
Training was conducted over 10 epochs, with checkpoints saved at regular intervals to monitor progress and performance.
Evaluation
Testing Data, Factors & Metrics
Testing Data
The testing data comprised patient-centered materials not included in the training set, evaluated for readability and comprehension improvement.
Factors
Evaluation factors included readability scores and patient comprehension levels.
Metrics
Metrics included SMOG, Flesch-Kincaid, and Gunning Fog scores, along with patient comprehension assessment through usability testing.
Results
The model demonstrated significant improvement in readability scores and patient comprehension compared to existing AI technologies.
Summary
The AI-driven tool effectively simplified medical texts to a 6th-grade reading level, enhancing understanding and engagement among patients with low health literacy.
Model Examination
The model's outputs were reviewed by healthcare professionals to ensure accuracy and completeness.
Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type: GPU (NVIDIA A100)
- Hours used: 120 hours
- Cloud Provider: AWS
- Compute Region: US West (Utah)
- Carbon Emitted: 500 kg CO2eq
Technical Specifications [optional]
Model Architecture and Objective
The model is based on a sequence-to-sequence transformer architecture fine-tuned for text simplification.
Compute Infrastructure
Hardware
Training was conducted on NVIDIA A100 GPUs.
Software
The model was developed on Google Colab using Python and Hugging Face's Transformers library.
Glossary
Health Literacy: The ability to obtain, process, and understand basic health information to make appropriate health decisions. Readability Assessments: Tools used to evaluate the reading level of a text, such as SMOG, Flesch-Kincaid, and Gunning Fog.
More Information
For further details and inquiries, please contact the model author.
Model Card Authors
Clark Parry
Model Card Contact
Visit [website] for business inquiries. Contact author for model inquiries.