From What to When: Engineering Proactive and Predictive Intelligence for Companion Animals

The evolution of pet-health monitoring requires a fundamental shift from recording what happened (passive tracking) to predicting when a clinical outcome will occur (predictive intelligence). This transition is hindered by the inherent 'messiness' of real-world behavioral data — asynchronous sampling, systematic biases, and extensive missing values. Drawing on the clinical data mining workflows of Stanford Medicine and the cognitive biology of Duke University, this paper presents the Barkley Deep Tech infrastructure. It demonstrates how electronic phenotyping and strategic feature engineering — grounded in biological knowledge — transform noisy sensor data into high-fidelity diagnostic signals. The paper specifically addresses the 'Missing Data Paradox' and the implementation of behavioral knowledge graphs as the foundation for a value-based, predictive veterinary ecosystem. Barkley White Paper | Precision Behavioral Intelligence Series | No. 02.