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

ARISTOTELES项目旨在建立一个多国协调数据平台，以开发和实施用于复杂疾病管理的新型人工智能（AI）方法，其中合并症的进展和表现是通过多种相互作用的途径进行的。我们的目标是将我们的新方法应用于因房颤（AF）而有巨大需求的人群，但我们的输出可以扩展到其他复杂的疾病。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的、负责任的和尊重患者/参与者数据的使用的支柱，以开发和验证新颖的、值得信赖的人工智能工具，从而实现更个性化的风险评估和管理。这代表了AF治疗的范式转变，从关注个体风险因素和选定结局（例如，中风）的治疗方法，支持及时的诊断和治疗干预，以减少疾病进展、残疾、住院和死亡，以及提高患者对生活方式改变、药物和其他治疗方案的依从性。ARISTOTELES将通过8个相互关联的工作包（WP）交付：WP 1是研究管理/协调。WP 2为开发可信赖的AI提供了道德/法律的要求。WP 3解决了利益相关者对AI的理解，需求评估以及参与AI开发的所有阶段。在WP 4中，关于基因型和表型特征的粒度数据从不同的数据集统一到一个通用平台中。在WP 5中，开发了AI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WP 6中，我们在临床试验模拟（计算机模拟试验）中测试了WP 5中开发的AI工具。在WP 7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推动临床实施和结果传播。

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.