Integrated AI-based Care Model Library I

Abstract

Deliverable D3.7, titled “Integrated AI-based Care Model Library I”, consolidates the state-of-the-art (SOTA) analysis, and the methodologies explored in tasks T3.5 – “Explainable Models and Algorithms for Trustworthy AI” and T3.6 – “Integrated Care Model Library: Implementation and Recalibration of Adaptive Models” of Work Package 3 (WP3 – “Data and AI Governance for Personalized Prediction, Monitoring and Recommendations”) of the project. Deliverable D3.7 is the first version of a two-part series of deliverables, namely D3.7 - “Integrated AI-based Care Model Library I” and D3.8 - “Integrated AI-based Care Model Library II” (to be submitted on M32). This deliverable D3.7 highlights the innovative research efforts being made in integrating sophisticated SOTA models and advanced Machine Learning (ML) technologies into the Virtual Health Platform (VHP). The deliverable further demonstrates how these advancements contribute to achieving the project’s overarching objectives. From the collective work conducted in WP3, this deliverable serves as both a practical guide and a repository of insights for stakeholders, transferring knowledge and fostering the adoption of advanced Integrated Care Model (ICM) technologies in healthcare. This document underscores the project’s dedication to driving impactful advancements and setting a benchmark for the future of predictive and preventive healthcare systems. There are several major challenges that should be addressed during the lifecycle of this project, which include: i) the integration of heterogeneous data sources into high-quality and bias-free datasets, ii) scalability and real-time processing of developed algorithms, iii) adaptability of AI/ML models to diverse and extremely complex healthcare settings, iv) explainability of AI models and their ethical compliance, and finally v) rigorous evaluation and validation. As this deliverable will demonstrate recent advancements in AI, and particularly multimodal processing can greatly assist in some of these challenges, given that they can process and integrate a wide range data sources to provide a holistic understanding of patient health with increased personalised accuracy and adaptability to new clinical settings.This report introduces key concepts in multimodal systems engineering as required for the development of the Integrated Care Model Library (ICML). This deliverable explores the advanced prediction analytical tools that will be developed to enhance the VHP and ultimately empower it with advanced AI methodologies. More specifically, the main objectives of this deliverable include the development of a novel methodology for ICML with an objective to promote accurate predictions and explainable outcomes across European populations irrespective of ethnicity or socioeconomic background. This deliverable also aims to tackle the ethical compliance aspects of the ICML and serve in the facilitation of a reliable framework that meaningfully assists clinicians in the decision-making process. Deliverable D3.7, titled “Integrated AI-based Care Model Library I,” establishes the foundation for achieving these objectives. More specifically, this document constitutes a detailed account of the state-of-the-art, methodologies, strategies, and outcomes associated with integrated care models, emphasising on its alignment with the broader goals of the COMFORTage project mission. The document bridges the gap between theoretical advancements and practical implementation, presenting solutions that are both scientifically robust and operationally feasible. A key focus of this deliverable is the alignment of project objectives with the complexity of the challenges identified. These challenges underscore the importance of a systematic and collaborative approach, as exemplified by the methodologies and frameworks described in this document.

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