This paper introduces a continuity-preserving, drift-detection framework for identifying early loss of coherence and stability in biological systems prior to overt clinical failure. Continuity is formalized as the preservation of physiological and contextual integrity across time, while drift is defined as a measurable deviation from an individual system’s expected biological trajectory. Rather than relying on static thresholds or population-level markers, the framework focuses on longitudinal change, internal consistency, and deviation from self-baseline behavior. The proposed approach is system-agnostic within the biological domain and is applicable to complex health processes such as neurodegenerative disease progression, chronic illness evolution, and other conditions characterized by gradual, non-linear change. The paper establishes theoretical foundations, defines core constructs, and outlines observable signals of biological drift without prescribing diagnostic criteria, interventions, or treatment thresholds. This work is intended to serve as a foundational reference for early anomaly detection, longitudinal health monitoring, and continuity-aware analysis of biological systems, supporting earlier insight into pathological change while remaining compatible with existing clinical and research practices.
Ben Quinn-Reed (Sat,) studied this question.