An Integrated Deep Learning and Graph-Theoretic Optimization Model for Real-Time Pattern Discovery in Health Information

Abstract

The continuous ingestion of high-dimensional, non-stationary physiological telemetry has precipitated a computational paradigm shift in Health Information Systems (HIS). Although deep sequence models have been proven to be highly accurate in clinical settings for pattern discovery, their deployment in real time is often limited by extreme latency of inference and an intrinsic lack of actionable clinical interpretation. In this work, the authors present a novel end-to-end hybrid system that aims to overcome this latency-accuracy dilemma by combining generalized graph theory and a Topology-Preserving Genetic Algorithm (TP-GA). The TP-GA is an intelligent, structural pre-processing filter which acts as a discretization of continuous phase spaces (obtained by empirical mining) to detect mathematically anomalous subgraphs deterministically. A novel Tensor Interface Layer then re-shapes these optimal graph trajectories into highly compressed input tensors that are then evaluated by a Transformer-based deep sequence backend. The integrated model obtained an Area Under the Precision-Recall Curve (AUPRC) of 0.952 and 0.974, respectively, on the low-frequency multi-parameter telemetry (MIMIC-III) and high-frequency univariate signals (MIT-BIH Arrhythmia Database). Importantly, the framework is able to pre-filter the sequence length before a neural network evaluation, avoiding the quadratic complexity problem of self-attention mechanisms. This structural compression achieved less than 19milliseconds of inference latency and compressed the active parameter footprint by more than 80%. In addition, the framework is clinically interpretable ante-hoc, and structurally limits the model's focus to the discrete, mappable transitions of physiological state. This approach creates a very viable, repeatable foundation for deep predictive intelligence to be deployed directly on high-end embedded systems or on resource constrained bedside edge devices.