Beck_Moulton

Posted on Jan 23

Predicting the Spike: Building a CGM Warning System with Transformers and PyTorch Forecasting

#webdev #ai #tutorial #python

In the world of Time Series Forecasting, managing non-stationary data like Continuous Glucose Monitoring (CGM) readings is a boss-level challenge. Traditional statistical models often fail because blood glucose isn't just a sequence of numbers; it’s a complex dance of insulin sensitivity, exercise, and the "carb-load" lag. Today, we’re moving beyond simple moving averages to leverage Transformer Architecture and Deep Learning to predict hyperglycemic events before they happen.

By using the Temporal Fusion Transformer (TFT), we can capture long-range dependencies—like how that pizza you ate three hours ago is suddenly wreaking havoc on your metabolic stability. If you've been looking to master HealthTech data pipelines or want to see how PyTorch Forecasting handles real-world chaos, you’re in the right place.

The Architecture: From Sensor to Prediction

Managing wearable data requires a robust pipeline. We aren't just training a model; we are building a reactive system. Here is how the data flows from a subcutaneous sensor to a high-latency alert.

graph TD
    A[CGM Sensor / Wearable] -->|Raw Glucose Values| B(InfluxDB)
    C[Nutritional Log / Apple Health] -->|Carb/Protein Inputs| B
    B --> D{Data Pre-processing}
    D -->|Feature Engineering| E[Pandas / TimeSeriesDataSet]
    E --> F[PyTorch Forecasting: TFT Model]
    F --> G[Probability Distribution of Future Glucose]
    G --> H[Grafana Dashboard / Alert System]
    H -->|Feedback Loop| F

Prerequisites

To follow this advanced tutorial, you'll need a environment with:

Python 3.9+
Tech Stack: PyTorch Forecasting, Pandas, InfluxDB-client, and Grafana for visualization.
A basic understanding of Attention mechanisms (but don't worry, we'll keep it practical).

Step 1: Connecting to the Source (InfluxDB)

CGM data is high-frequency. InfluxDB is our choice here because of its native handling of time-series retention policies.

import pandas as pd
from influxdb_client import InfluxDBClient

# Connecting to our health data bucket
client = InfluxDBClient(url="http://localhost:8086", token="my-token", org="wellally")
query_api = client.query_api()

query = """from(bucket: "health_metrics")
  |> range(start: -7d)
  |> filter(fn: (r) => r["_measurement"] == "glucose")
  |> pivot(rowKey:["_time"], columnKey: ["_field"], valueColumn: "_value")"""

df = query_api.query_data_frame(query)
df['_time'] = pd.to_datetime(df['_time'])
print(f"✅ Loaded {len(df)} glucose data points.")

Step 2: Feature Engineering for Metabolic Context

A Transformer is only as good as the context you give it. Since glucose levels are non-stationary, we need to inject "Known Reals" (like time of day) and "Observed Reals" (like previous glucose values).

def prepare_data(df):
    # Add time-based features
    df["hour"] = df['_time'].dt.hour.astype(str).astype("category")
    df["day_of_week"] = df['_time'].dt.dayofweek.astype(str).astype("category")

    # Create a relative time index for PyTorch Forecasting
    df["time_idx"] = (df["_time"] - df["_time"].min()).dt.total_seconds() // 300 # 5-min intervals
    df["time_idx"] = df["time_idx"].astype(int)

    # Grouping by User ID (even if it's just one)
    df["group"] = "user_01"

    return df

df_cleaned = prepare_data(df)

Step 3: Defining the Temporal Fusion Transformer (TFT)

The Temporal Fusion Transformer is the "Gold Standard" for time series because it uses specialized "Gated Residual Networks" to select relevant features.

Pro Tip: If you're looking for more production-ready patterns on integrating AI with health devices, check out the deep dives at wellally.tech/blog. They cover advanced architectural patterns for low-latency inference in medical IoT.

from pytorch_forecasting import TimeSeriesDataSet, TemporalFusionTransformer
from pytorch_forecasting.metrics import QuantileLoss

# Define the dataset parameters
max_prediction_length = 12  # Predict next 60 minutes (12 * 5 mins)
max_encoder_length = 48     # Look back at last 4 hours

training_cutoff = df_cleaned["time_idx"].max() - max_prediction_length

training = TimeSeriesDataSet(
    df_cleaned[lambda x: x.time_idx <= training_cutoff],
    time_idx="time_idx",
    target="glucose_level",
    group_ids=["group"],
    min_encoder_length=max_encoder_length // 2,
    max_encoder_length=max_encoder_length,
    min_prediction_length=1,
    max_prediction_length=max_prediction_length,
    static_categoricals=["group"],
    time_varying_known_categoricals=["hour"],
    time_varying_known_reals=["time_idx"],
    time_varying_unknown_reals=["glucose_level", "carbs_intake"],
    target_normalizer=None,  # Glucose is usually within a specific range
    add_relative_time_idx=True,
    add_target_scales=True,
    add_encoder_grad_in_clouds=True,
)

# Initialize the model
tft = TemporalFusionTransformer.from_dataset(
    training,
    learning_rate=0.03,
    hidden_size=16, 
    attention_head_size=4,
    dropout=0.1,
    hidden_continuous_size=8,
    loss=QuantileLoss(), # We want prediction intervals, not just a single line!
    log_interval=10,
    reduce_on_plateau_patience=4,
)
print(f"🚀 Model initialized with {tft.size()/1e3:.1f}k parameters.")

Step 4: Visualizing the Future (Grafana Integration)

Predicting a spike is useless if the user doesn't see it. We push our predicted quantiles (10%, 50%, 90%) back to InfluxDB, which Grafana then picks up to show a "shadow" of potential future values.

The 90th Quantile: This is our "Warning" line. If this crosses 180mg/dL, we trigger an alert.
The 50th Quantile: The most likely trajectory.

Why this beats LSTM?

LSTMs often suffer from "vanishing gradients" and have a hard time weighing a meal eaten 2 hours ago against a walk taken 10 minutes ago. The Transformer's Multi-Head Attention allows the model to look back at specific "pulses" in the data (like a high-carb meal) regardless of how many time steps have passed.

Conclusion: Health is the Ultimate Time Series

Building a CGM peak warning model isn't just about code; it's about understanding the nuances of human biology through the lens of data. By combining PyTorch Forecasting with a solid InfluxDB/Grafana stack, you can create a system that truly improves lives.

For more advanced tutorials on building high-performance AI systems and exploring the intersection of technology and wellness, head over to the WellAlly Blog.

What's next?

Try adding "Heart Rate" as a time-varying covariate.
Experiment with different QuantileLoss weights to reduce false-positive alarms.

Happy coding, and stay healthy!

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