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

The Architecture: From Sensor to Prediction ## Prerequisites ## Step 1: Connecting to the Source (InfluxDB) ## Step 2: Feature Engineering for Metabolic Context ## Step 3: Defining the Temporal Fusion Transformer (TFT) ## Step 4: Visualizing the Future (Grafana Integration) ## Why this beats LSTM? ## Conclusion: Health is the Ultimate Time Series 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. 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. To follow this advanced tutorial, you'll need a environment with: CGM data is high-frequency. InfluxDB is our choice here because of its native handling of time-series retention policies. 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). 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. 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. 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. 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. Happy coding, and stay healthy! If you enjoyed this, don't forget to **heart* this post and follow for more deep dives into the world of AI and Wearables!* Templates let you quickly answer FAQs or store snippets for re-use. Are you sure you want to hide this comment? It will become hidden in your post, but will still be visible via the comment's permalink. Hide child comments as well For further actions, you may consider blocking this person and/or reporting abuse COMMAND_BLOCK: 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 Enter fullscreen mode Exit fullscreen mode COMMAND_BLOCK: 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 COMMAND_BLOCK: 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 COMMAND_BLOCK: 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.") Enter fullscreen mode Exit fullscreen mode COMMAND_BLOCK: 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.") COMMAND_BLOCK: 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.") COMMAND_BLOCK: 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) Enter fullscreen mode Exit fullscreen mode COMMAND_BLOCK: 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) COMMAND_BLOCK: 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) COMMAND_BLOCK: 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.") Enter fullscreen mode Exit fullscreen mode COMMAND_BLOCK: 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.") COMMAND_BLOCK: 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.") - 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). - The 90th Quantile: This is our "Warning" line. If this crosses 180mg/dL, we trigger an alert. - The 50th Quantile: The most likely trajectory. - Try adding "Heart Rate" as a time-varying covariate. - Experiment with different QuantileLoss weights to reduce false-positive alarms.