Model Card for Gluformer Blood Glucose Prediction Model
This model uses past continuous glucose monitor (CGM) values to predict values for the next hour.
Model Details
Model Description
- Developed by: Renat Sergazinov, Mohammadreza Armandpour, Irina Gaynanova
- Funded by: Texas A&M University
- Shared by: Nat Jeffries
- Model type: Time series encoder-decoder Transformer
Model Sources
How to Get Started with the Model
Use the code below to get started with the model.
from transformers import AutoModel, AutoConfig
from datetime import timedelta, datetime
model = AutoModel.from_pretrained('njeffrie/Gluformer', trust_remote_code=True)
config = AutoConfig.from_pretrained('njeffrie/Gluformer', trust_remote_code=True)
# Dummy input and timestamp values.
input_glucose = [100.0 for _ in range(config.len_seq)]
timestamps = [datetime(2025, 7, 25, 0, 0) + timedelta(minutes=5 * i) for i in range(len(input_glucose))]
subject_id = 0
pred, log_var = model(subject_id, timestamps, input_glucose)
Predictions will be predicted future glucose values in 5 minute increments. Log var indicates confidence. See the paper for more details.
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