Most digital health systems rely on population-trained models and apply personalization by adjusting predictions or heuristics at the individual level. This approach assumes that individual behavior can be inferred from conditioned population averages.
In real-world settings, individual data is often sparse, irregular, and non-ergodic. Under these conditions, population-derived inference can fail to reflect individual dynamics.
Trillies+ is built on a causal reasoning framework called Personal Intelligence (PI) which is designed to support N-of-1 inference under sparse, non-ergodic real-world data by reasoning directly from an individual’s own longitudinal evidence.
This architectural separation allows Trillies+ to operate under real-world data constraints without assuming population-level generalizability.
Additional details on the underlying reasoning framework are available here: Personal Intelligence (PI).