Context-Aware Longitudinal Patient Health Intelligence for Personalized Early Detection of Health Anomalies
Year: 2026
Type: article
Abstract: Longitudinal patient data from electronic health records, laboratory systems, wearable sensors, and remote monitoring platforms provide an opportunity to identify clinically meaningful changes before deterioration becomes obvious.A persistent challenge, however, is that conventional monitoring frequently relies on population-level thresholds, short temporal windows, or historical information represented implicitly inside complex predicftive models.This paper presents a contextaware longitudinal patient health intelligence system in which contextual memory is treated as an explicit architectural component.The system continuously represents patient-specific historical behavior, evaluates new observations against an evolving individual baseline, and updates memory through a controlled adaptation mechanism.Anomaly detection is therefore framed as a memoryconditioned deviation problem rather than as a comparison with a universal population norm.The proposed architecture integrates heterogeneous data ingestion, preprocessing, contextual memory management, anomaly scoring, visualization, and human-in-the-loop clinical decision support.The source study evaluates the approach using real-world longitudinal healthcare datasets and controlled synthetic simulations, comparing it with population-based threshold monitoring, sliding-window monitoring, and memory-free statistical detection.The reported findings indicate improved anomalydetection performance, lower false alarm rates, improved sensitivity to gradual deterioration, and stronger interpretability.Analytical and sensitivity results further show that contextual memory can remain bounded and stable while adapting to sustained physiological change.The work demonstrates that explicit longitudinal context can provide a practical bridge between simple statistical monitoring and more opaque learning-based healthcare AI.
Author: Hye-Seen Kang
Institution: Chonnam National University
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: bronze
OpenAlex ID: W7224090617