Time-Series Alchemy: Predicting Glucose Trends 2 Hours Out with Transformers and PyTorch Lightning

Source: Dev.to

Transformer Architecture for Glucose Forecasting

graph TD
    A[InfluxDB: Raw CGM Data] --> B[Pandas: Feature Engineering]
    B --> C[Scikit-learn: Scalers & Windowing]
    C --> D[Transformer Encoder]
    D --> E[Self-Attention Layers]
    E --> F[Linear Projection Head]
    F --> G[2‑Hour Forecast: 24 Intervals]
    G --> H{Insight: Hypo/Hyper Alert}

The model ingests the last 6 hours of CGM data (72 points at a 5‑minute sampling rate) and predicts the next 2 hours (24 points). Self‑attention lets the network weigh any past observation—whether a jog 30 minutes ago or a carb‑rich meal two hours earlier—without the forgetting issues typical of RNNs/LSTMs.

Required Stack

Fetching CGM Data from InfluxDB

import pandas as pd
from influxdb_client import InfluxDBClient

def fetch_cgm_data(bucket: str, org: str, token: str, url: str) -> pd.DataFrame:
    client = InfluxDBClient(url=url, token=token, org=org)
    query = f'''
    from(bucket: "{bucket}")
      |> range(start: -7d)
      |> filter(fn: (r) => r["_measurement"] == "glucose_level")
      |> pivot(rowKey:["_time"], columnKey: ["_field"], valueColumn: "_value")
    '''
    df = client.query_api().query_data_frame(query)
    df['_time'] = pd.to_datetime(df['_time'])
    return df.set_index('_time')

# Example usage:
# df = fetch_cgm_data("health_metrics", "my_org", "SECRET_TOKEN", "http://localhost:8086")

Preparing the Data

from sklearn.preprocessing import StandardScaler

# Assume the raw glucose column is named 'glucose'
scaler = StandardScaler()
df['glucose_scaled'] = scaler.fit_transform(df[['glucose']])

Standardizing the signal (≈ 0 ± 1) is essential; raw CGM values range from 40 to 400 mg/dL and can destabilize training.

Model Definition

import torch
import torch.nn as nn
import pytorch_lightning as pl

class GlucoseTransformer(pl.LightningModule):
    def __init__(
        self,
        input_dim: int = 1,
        model_dim: int = 64,
        n_heads: int = 4,
        n_layers: int = 3,
        output_dim: int = 24,
    ):
        super().__init__()
        self.save_hyperparameters()

        # Project raw input to model dimension
        self.input_fc = nn.Linear(input_dim, model_dim)

        # Learnable positional encoding (max seq length = 500)
        self.pos_encoder = nn.Parameter(torch.zeros(1, 500, model_dim))

        encoder_layer = nn.TransformerEncoderLayer(
            d_model=model_dim, nhead=n_heads, batch_first=True
        )
        self.transformer_encoder = nn.TransformerEncoder(
            encoder_layer, num_layers=n_layers
        )

        # Map the final hidden state to the 24‑step forecast
        self.output_fc = nn.Linear(model_dim, output_dim)
        self.loss_fn = nn.MSELoss()

    def forward(self, x):
        # x: [batch, seq_len, features]
        x = self.input_fc(x) + self.pos_encoder[:, : x.size(1), :]
        x = self.transformer_encoder(x)
        # Use the last time step's representation for prediction
        return self.output_fc(x[:, -1, :])

    def training_step(self, batch, batch_idx):
        x, y = batch
        y_hat = self(x)
        loss = self.loss_fn(y_hat, y)
        self.log("train_loss", loss, prog_bar=True)
        return loss

    def configure_optimizers(self):
        return torch.optim.Adam(self.parameters(), lr=1e-3)

Training Setup

# Instantiate the model
model = GlucoseTransformer()

# Lightning trainer (auto‑detect GPU/CPU, single device for demo)
trainer = pl.Trainer(max_epochs=50, accelerator="auto", devices=1)

# Fit the model (replace `train_dataloader` with your DataLoader)
# trainer.fit(model, train_dataloader)

Production Considerations

• Sensor drift & missing data – implement interpolation or masking strategies before feeding batches to the model.
• Uncertainty estimation – consider quantile regression or Monte‑Carlo dropout to surface confidence intervals.
• Edge deployment – see the WellAlly Tech Blog for HIPAA‑compliant pipelines and real‑time inference on wearable devices.

What’s Next?

• Multimodal inputs – add carbohydrate intake, step count, or insulin dosage as extra columns (input_dim > 1).
• Quantile regression – predict lower/upper percentiles (e.g., 5 % and 95 %) to generate prediction bands.
• Continuous learning – set up a retraining schedule that incorporates newly streamed CGM data.

For a deeper dive into scaling health‑tech infrastructure, check out the WellAlly Blog (search for “digital health scaling” and “edge AI for wearables”).

Feel free to experiment, share your results, and help shape the future of proactive health monitoring!