Applying ARtificial Intelligence to define clinical trajectorieS for personalized predicTiOn and early deTEction of comorbidity and muLtimorbidity pattErnS (ARISTOTELES)

应用人工智能定义临床轨迹，以进行个性化预测以及合并症和多发病模式的早期检测（亚里士多德）

• 批准号: 10102350
• 金额: $ 40万
• 依托单位: LIVERPOOL JOHN MOORES UNIVERSITY
• 依托单位国家: 英国
• 项目类别: EU-Funded
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=10102350
• 关键词: Applying; ARtificial; Intelligence; define; clinical

The ARISTOTELES project aimsto build a multinational harmonized data platform to develop and implement novel artificial intelligence (AI) approaches for management of complex diseases, where progression and manifestations of comorbidities are via multiple interacting pathways. We aim to apply our novel approach to a population of great need due to atrial fibrillation (AF), but our outputs can be extended to other complex diseases with multimorbidity. By integrating AIs into clinical practice, our platform will form a backbone for acceptable, responsible, and respectful uses of patient/participant data to develop and validate novel trustworthy AI tools for more personalized risk assessment and management. This represents a paradigm shift in AF treatment, moving from a focus on individual risk factors and selected outcomes (eg. stroke) to a holistic approach, underpinning timely diagnostic and therapeutic interventions to reduce disease progression, disability, hospitalizations and mortality, as well as improve patient adherence to lifestyle modifications, medications, and other treatment regimens. ARISTOTELES will be delivered through 8 inter-linking work-packages (WPs): WP1 is study management/coordination. WP2 provides the ethical/legal requirements for the development of a trustworthy AI. WP3 addresses stakeholder understanding of AI, needs assessment, and engagement in all the phases of the AI development. In WP4, granular data on genotype and phenotype characteristics, are harmonized from different datasets into a common platform. In WP5, AI algorithms/tools are developed and connected to an interactive output interface for patients and clinicians. In WP6, we test the AI tool developed in WP5 in a clinical trial simulation (in silico trial). In WP7 a multicenter randomized trial runs across multiple countries including both primary care and secondary care. WP7 and WP8 drive the clinical implementation and dissemination of results.

亚里士多德项目旨在建立一个多国统一数据平台，以开发和实施用于管理复杂疾病的新的人工智能(AI)方法，其中并存疾病的进展和表现是通过多个相互作用的路径进行的。我们的目标是将我们的新方法应用于因心房颤动(AF)而极有需求的人群，但我们的成果可以扩展到其他具有多重合并症的复杂疾病。通过将人工智能集成到临床实践中，我们的平台将成为可接受、负责任和尊重使用患者/参与者数据的骨干，以开发和验证新的值得信赖的人工智能工具，以实现更个性化的风险评估和管理。这代表着房颤治疗的范式转变，从关注个人风险因素和选定的结果(例如。将治疗(中风)转变为一种全面的方法，为及时的诊断和治疗干预奠定基础，以减少疾病进展、残疾、住院和死亡率，并提高患者对改变生活方式、药物和其他治疗方案的依从性。亚里士多德将通过8个相互链接的工作包(WPS)交付：WP1是研究管理/协调。WP2为开发可信赖的人工智能提供了伦理/法律要求。WP3解决了利益相关者对人工智能的理解、需求评估以及对人工智能开发的所有阶段的参与。在WP4中，来自不同数据集的关于基因型和表型特征的细粒度数据被协调到一个公共平台中。在WP5中，开发了人工智能算法/工具，并将其连接到患者和临床医生的交互式输出界面。在WP6中，我们在临床试验模拟中(在Silico试验中)测试了在WP5中开发的AI工具。在WP7中，一项多中心随机试验在多个国家进行，包括初级保健和二级保健。WP7和WP8推动了结果的临床实施和传播。