# A data-driven AI framework for personalized diagnosis, prognosis, and therapeutic optimization in chronic disease management using multimodal big data analytics

## TL;DR

This paper introduces an AI framework that uses big data to personalize diagnosis, prognosis, and treatment for chronic diseases.

## Contribution

The novel framework combines a patient-specific tensor network with adaptive intervention strategies for chronic disease management.

## Key findings

- The framework achieved superior outcome prediction accuracy on large-scale datasets.
- It demonstrated effective intervention personalization and trajectory alignment in chronic care.
- The system shows practical applicability for scalable, intelligent chronic disease management.

## Abstract

The transformation of chronic disease management is increasingly driven by the integration of AI and multimodal data analytics, enabling precise, individualized, and scalable healthcare interventions. Despite the growing availability of longitudinal and heterogeneous health data, conventional methods are constrained in their ability to model the complex, patient-specific dynamics inherent to chronic conditions. Traditional clinical decision support systems rely on rigid, population-level models that inadequately address inter-patient variability, multi-condition comorbidities, and evolving disease trajectories.

To overcome these limitations, we propose a computational framework that utilizes multimodal big data to enable personalized diagnosis, prognosis, and therapeutic optimization. At the core of this framework is the Patient-Adaptive Transition Tensor Network (PATTN), a tensorized dynamical model that captures individual-specific disease evolution through structured latent state representations and high-order temporal dependencies. Complementing this is the Trajectory-Aligned Intervention Recalibration (TAIR), an adaptive decision-making strategy that continuously aligns predicted and observed health trajectories, facilitating real-time treatment policy refinement. This unified pipeline integrates latent trajectory modeling, condition-aware modular representation, and personalized policy optimization.

Experimental evaluations on large-scale multimodal datasets demonstrate superior performance in outcome prediction accuracy, intervention personalization, and trajectory alignment, underscoring the practical applicability of the system in chronic care settings. By combining patient-specific temporal modeling with adaptive therapeutic recalibration, this framework represents a significant advancement toward scalable, intelligent, and individualized chronic disease management leveraging AI and big data infrastructures.