Agent-based Modeling for Ontology-driven Analysis of Patient Trajectories

Area of Science:

• Digital Health
• Artificial Intelligence in Healthcare
• Patient-Centered Care

Background:

• Post-discharge medical treatments pose challenges for patients managing new routines and lifestyles without constant professional support.
• Existing technological solutions like mobile apps and wearables often lack personalization, failing to account for individual patient characteristics and contexts.
• Patient trajectories, representing individual health events and circumstances, offer a framework for understanding patient needs but are difficult to integrate into decentralized eHealth data analytics.

Purpose of the Study:

• To propose a novel multi-agent paradigm for modeling eHealth support systems centered on patient trajectories.
• To develop a methodology for effectively utilizing patient trajectories in decentralized eHealth systems for personalized support.
• To demonstrate the application of this approach using a case scenario for cancer survivor support.

Main Methods:

• Developed a multi-agent architecture where patient trajectories are semantically represented.
• Utilized semantic representations of individual treatment pathways for information exchange.
• Designed the system for potential integration with Artificial Intelligence (AI) systems for prediction and classification tasks.

Main Results:

• The proposed agent-based architecture provides a framework for modeling eHealth support systems based on patient trajectories.
• Semantic representations facilitate the exchange of patient-relevant information within decentralized systems.
• The approach is adaptable for various conditions, with a demonstrated case for cancer survivor support.

Conclusions:

• Patient trajectories are crucial for developing personalized and effective eHealth support systems.
• The multi-agent paradigm offers a viable solution for integrating patient trajectories into decentralized eHealth analytics.
• This methodology holds promise for improving patient outcomes and support in various healthcare settings.