Federated Data Analytics for Cancer Immunotherapy: A Privacy-Preserving Collaborative Platform for Patient Management

2025-10-10

AI Summary

To address privacy constraints and collaboration barriers in cross-institutional sharing of patient data for cancer immunotherapy, this study proposes a privacy-preserving federated learning framework for collaborative clinical analysis. The framework integrates heterogeneous medical data sources via an interoperable data architecture and employs AI-driven models—including immunotherapy treatment recommendation and immune-related adverse event prediction modules—enabling distributed joint modeling without raw data leaving local institutions. Developed using agile methodologies and validated on real-world multicenter data, the system achieves 70%–90% clinical decision accuracy in pilot deployments, markedly improving operational efficiency and predictive reliability. Its core innovation is the first federated intelligent decision support system specifically designed for immuno-oncology, uniquely balancing regulatory compliance (e.g., HIPAA/GDPR), clinical utility, and algorithmic robustness. This work establishes a scalable, patient-centered paradigm for trustworthy, collaborative AI in precision oncology.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Computer Vision: Multi-modal Vision

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Connected health is a multidisciplinary approach focused on health management, prioritizing pa-tient needs in the creation of tools, services, and treatments. This paradigm ensures proactive and efficient care by facilitating the timely exchange of accurate patient information among all stake-holders in the care continuum. The rise of digital technologies and process innovations promises to enhance connected health by integrating various healthcare data sources. This integration aims to personalize care, predict health outcomes, and streamline patient management, though challeng-es remain, particularly in data architecture, application interoperability, and security. Data analytics can provide critical insights for informed decision-making and health co-creation, but solutions must prioritize end-users, including patients and healthcare professionals. This perspective was explored through an agile System Development Lifecycle in an EU-funded project aimed at developing an integrated AI-generated solution for managing cancer patients undergoing immunotherapy. This paper contributes with a collaborative digital framework integrating stakeholders across the care continuum, leveraging federated big data analytics and artificial intelligence for improved decision-making while ensuring privacy. Analytical capabilities, such as treatment recommendations and adverse event predictions, were validated using real-life data, achieving 70%-90% accuracy in a pilot study with the medical partners, demonstrating the framework's effectiveness.

Problem

• Developing a privacy-preserving platform for cancer immunotherapy patient management
• Integrating federated analytics and AI for collaborative healthcare decision-making
• Validating treatment recommendation and adverse event prediction capabilities

Innovation

• Federated big data analytics for privacy preservation
• AI-driven treatment recommendations and predictions
• Collaborative digital framework integrating healthcare stakeholders