CGMformer : A generative pretrained transformer for predicting and decoding individual glucose dynamics from continuous glucose monitoring data
pip install -r requirements.txt
Different CGM data have different attributes, we recommend to refer to the processing_811_data.ipynb to process your data, where the continuous glucose data are labeled with the key "input_ids".
In build_vocab.ipynb, we generate a vocab from 39-301 and containing <MASK>, <PAD>, <CLS> token.
To train CGMformer using unlabeled CGM data, use the run_pretrain_CGMFormer.py script.
deepspeed --num_gpus={num_gpus} run_pretrain_CGMFormer.py
where
num_gpus: number of GPUs used for training
python run_clustering.py --checkpoint_path /path/to/checkpoint --data_path /path/to/data --save_path /path/to/save
python run_labels_classify.py --checkpoint_path /path/to/checkpoint --train_path /path/to/train_data --test_path /path/to/test_data --output_path /path/to/save
To training CGMformer_C, paired CGM data and clinical data including age, bmi, fpg, ins0, HOMA-IS, HOMA-B, pg120, hba1c, hdl are needed:
python SupervisedC.py
To calculate CGMformer_C from trained model and embedded vectors from CGMformer:
python CalculateSC.py
CGMformer_type provides subtyping based on CGM data. Embedded vectors from CGMformer are required.
python Classifier.py
Paired embedded vector, meal nutrition information, and before (and post) meal glucose are required for (training) CGMformer_Diet:
python PredictGlucose.py