Symptom Radar: Exploring the Transition from Reactive Detection to Proactive Prediction [In-Person Only]

Modern healthcare faces a critical limitation: it typically intervenes only after symptoms manifest. Wearable technology has begun to bridge this gap by enabling continuous, longitudinal monitoring, providing users with unprecedented visibility into their daily physiology. Yet, the vast potential of this data remains largely untapped: the opportunity to shift from high-resolution observation to proactive prediction. In this talk, we share the development journey of Oura’s "Symptom Radar," a feature explicitly engineered to test the limits of shifting this paradigm from reactive detection to proactive prediction.

We invite the audience to explore our central hypothesis: that high-frequency physiological time series can outpace human perception, allowing detection algorithms to function as early warning systems. We discuss the unique machine learning challenges of validating this theory, particularly when training on "ground truth" labels (user tags) that are subjective and inherently delayed. We outline our high-level technical design, prioritizing rigorous data preparation and specific modeling principles to maximize predictive power and rigorously evaluate the system's effectiveness.

The discussion centers on our real-world findings. We present evidence that in certain percentage of detected cases, our models identify significant biometric shifts—led primarily by temperature trends—well before users report symptoms. We examine how increasing data scale linearly improves this "prediction gap," and discuss how these insights provide the necessary blueprint for future foundation models capable of transforming personal health monitoring from a retrospective report into a prospective forecast.