cehrgpt

CEHRGPT

Description

CEHRGPT is a synthetic data generation model developed to handle structured electronic health records (EHR) with enhanced privacy and reliability. It leverages state-of-the-art natural language processing techniques to create realistic, anonymized patient data that can be used for research and development without compromising patient privacy.

Features

• Synthetic Patient Data Generation: Generates comprehensive patient profiles including demographics, medical history, treatment courses, and outcomes.
• Privacy-Preserving: Implements techniques to ensure the generated data does not reveal identifiable information.
• Compatibility with OMOP: Fully compatible with the OMOP common data model, allowing seamless integration with existing healthcare data systems.
• Extensible: Designed to be adaptable to new datasets and different EHR systems.

Installation

To install CEHRGPT, clone this repository and install the required dependencies.

git clone https://github.com/knatarajan-lab/cehrgpt.git
cd cehrgpt
pip install .

Pretrain

Pretrain cehrgpt using the Hugging Face trainer, the parameters can be found in the sample configuration yaml

mkdir test_results
# This is NOT required when streaming is set to true
mkdir test_dataset_prepared
python -u -m cehrgpt.runners.hf_cehrgpt_pretrain_runner sample_configs/cehrgpt_pretrain_sample_config.yaml

Generate synthetic sequences

Generate synthetic sequences using the trained model

export TRANSFORMERS_VERBOSITY=info
export CUDA_VISIBLE_DEVICES="0"
python -u -m cehrgpt.generation.generate_batch_hf_gpt_sequence \
  --model_folder test_results \
  --tokenizer_folder test_results \
  --output_folder test_results \
  --num_of_patients 128 \
  --batch_size 32 \
  --buffer_size 128 \
  --context_window 1024 \
  --sampling_strategy TopPStrategy \
  --top_p 1.0 --temperature 1.0 --repetition_penalty 1.0 \
  --epsilon_cutoff 0.00 \
  --demographic_data_path sample_data/pretrain

Convert synthetic sequences to OMOP

# omop converter requires the OHDSI vocabulary
export OMOP_VOCAB_DIR = ""
# the omop derived tables need to be built using pyspark
export SPARK_WORKER_INSTANCES="1"
export SPARK_WORKER_CORES="8"
export SPARK_EXECUTOR_CORES="2"
export SPARK_DRIVER_MEMORY="2g"
export SPARK_EXECUTOR_MEMORY="2g"

# Convert the sequences, create the omop derived tables
sh scripts/omop_pipeline.sh \
  test_results/top_p10000/generated_sequences/ \
  test_results/top_p10000/restored_omop/ \
  $OMOP_VOCAB_DIR

Citation

@article{cehrgpt2024,
  title={CEHRGPT: Synthetic Data Generation for Electronic Health Records},
  author={Natarajan, K and others},
  journal={arXiv preprint arXiv:2402.04400},
  year={2024}
}