构建隐私保护的联邦数据分析平台，助力癌症免疫治疗患者管理。
Federated Data Analytics for Cancer Immunotherapy: A Privacy-Preserving Collaborative Platform for Patient Management
- 采用联邦学习整合多方医疗数据，保护患者隐私。
- 在真实数据上验证，治疗建议与不良反应预测准确率达70%-90%。
- 适合关注医疗协作与数据安全的临床研究者与系统开发者。
连通健康是一种多学科方法，以患者需求为核心，推动工具、服务和治疗的开发。该范式通过及时共享准确患者信息，实现主动高效的照护。数字技术与流程创新的发展有望通过整合多种医疗数据源，提升连通健康水平，实现个性化医疗、健康结果预测和患者管理优化，但数据架构、应用互操作性与安全仍存挑战。数据分析可为决策提供关键洞察，但解决方案必须优先考虑患者与医疗专业人员。本研究基于欧盟资助项目，采用敏捷系统开发周期，开发了一套集成AI的数字解决方案，用于管理接受免疫治疗的癌症患者。本文提出一种跨照护链条的协作数字框架，利用联邦大数据分析与人工智能，支持更优决策并保障隐私。治疗建议与不良事件预测等分析能力在真实数据上验证，试点研究中准确率达70%-90%，证明了该框架的有效性。
原文摘要 · Abstract (English)
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.
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