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

ARISTOTELES 项目旨在建立一个跨国协调数据平台，以开发和实施用于管理复杂疾病的新型人工智能 (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实现的。我们的目标是将我们的新方法应用于因心房颤动（AF）而急需的人群，但我们的成果可以扩展到其他具有多发病率的复杂疾病。通过将人工智能融入临床实践，我们的平台将成为可接受、负责任和尊重地使用患者/参与者数据的支柱，以开发和验证新颖的值得信赖的人工智能工具，以实现更加个性化的风险评估和管理。这代表了房颤治疗的范式转变，从关注个体危险因素和选定的结果（例如中风）转向整体方法，支持及时的诊断和治疗干预，以减少疾病进展、残疾、住院和死亡率，并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) 进行交付：WP1 是研究管理/协调。 WP2 为开发值得信赖的人工智能提供了道德/法律要求。 WP3 解决了利益相关者对人工智能的理解、需求评估以及人工智能开发所有阶段的参与。在 WP4 中，有关基因型和表型特征的精细数据从不同的数据集中统一到一个通用平台中。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（计算机试验）中测试了 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.