A data-driven AI framework for personalized diagnosis, prognosis, and therapeutic optimization in chronic disease...

Area of Science:

• Artificial Intelligence in Healthcare
• Big Data Analytics
• Computational Biology

Background:

• Chronic disease management faces challenges with conventional methods due to patient variability and complex disease dynamics.
• Existing clinical decision support systems struggle with individualized care and evolving health trajectories.
• The integration of AI and multimodal data offers potential for precise, scalable healthcare interventions.

Purpose of the Study:

• To propose a computational framework leveraging multimodal big data for personalized diagnosis, prognosis, and therapeutic optimization in chronic diseases.
• To introduce the Patient-Adaptive Transition Tensor Network (PATTN) for modeling individual-specific disease evolution.
• To present Trajectory-Aligned Intervention Recalibration (TAIR) for adaptive, real-time treatment policy refinement.

Main Methods:

• Developed a computational framework integrating multimodal big data analytics.
• Utilized the Patient-Adaptive Transition Tensor Network (PATTN), a tensorized dynamical model, for latent state representation and temporal dependency capture.
• Implemented Trajectory-Aligned Intervention Recalibration (TAIR) for adaptive decision-making and treatment policy refinement.
• Integrated latent trajectory modeling, condition-aware modular representation, and personalized policy optimization.

Main Results:

• Demonstrated superior performance in outcome prediction accuracy on large-scale multimodal datasets.
• Showcased enhanced intervention personalization and trajectory alignment capabilities.
• Validated the practical applicability of the AI framework in chronic care settings.

Conclusions:

• The proposed framework significantly advances scalable, intelligent, and individualized chronic disease management.
• Combining patient-specific temporal modeling with adaptive therapeutic recalibration addresses limitations of traditional approaches.
• Leveraging AI and big data infrastructures is crucial for the future of personalized chronic care.