Stratification of Patients using advanced Integrative modelling of Data Routinely acquired for diagnosing Rheumatic complaints

使用常规获取的用于诊断风湿病症状的先进数据综合模型对患者进行分层

• 批准号: 10066059
• 金额: $ 50.65万
• 依托单位: NEWCASTLE 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=10066059
• 关键词: Stratification; Patients; using; advanced; Integrative

Globally 1.71 billion people have musculoskeletal symptoms, the leading contributor to disability. Early disease stratification is important to ensure appropriate care (most suited healthcare provider and best treatment choice). Currently the patient journey to diagnosis and effective treatment is long and inefficient, resulting in persistent disease burden and economic loss. This is due to insufficiently understood disease causes, similarities in symptoms between diseases, insufficiently discriminatory diagnostic tests and a “ trial and error” approach in initial treatment. SPIDeRR aims to disentangle the real-life complexity of early diagnosis of rheumatic diseases by considering the complete web of factors influencing patients’ symptoms. SPIDeRR’s approach will go well beyond the state-of-the-art in the following ways: -By identifying different disease groups, requiring different therapies, amongst patients with similar symptoms in contrast to the traditional approach aiming to only capture one disease early. -?By integrating all relevant data dimensions from every healthcare level (primary and secondary care and patients seeking advice online). -?By translating and applying machine learning techniques from the “omics” field to clinical patient data, which will result in new pipelines for translational data science SPIDERR will deliver three clinical models -a symptom checker for patients -a decision support tool for (primary) care providers providing guiding additional examination and referral decisions -a patient-patient similarity network to optimise diagnostic groups in rheumatology and support treatment decision To achieve this we additionally deliver solutions for data integration and shared analyses though GDPR compliant digital research environment and federated learning pipelines. Finally we will test the acceptability of the models through stakeholders studies and provide an implementation scene tailored to current healthcare in Europe.

全球17.1亿人患有肌肉骨骼症状，这是残疾的主要因素。早期疾病分层对于确保适当的护理（最适合的医疗保健提供者和最佳治疗选择）很重要。目前，患者进行诊断和有效治疗的旅程漫长而无效，导致持续性疾病伯恩和经济损失。这是由于不足以理解疾病原因，疾病之间的症状相似性，不足的歧视性诊断测试以及初始治疗中的“反复试验”方法。 Spiderr旨在通过考虑影响患者症状的完全因素的完整网络来消除风湿病早期诊断的现实生活中的复杂性。 Spiderr的方法将远远超出以下方式： - 通过识别不同疾病组，需要不同疗法，与具有相似症状相似的患者与传统方法相比，目的是较早地捕获一种疾病。 - ？通过整合每个医疗保健水平的所有相关数据维度（初级和二级护理以及在线寻求建议的患者）。 - ？通过翻译和应用机器学习技术从“ OMICS”字段到临床患者数据，这将导致新的转化数据流入渠道，转化数据科学蜘蛛侠将提供三个临床模型 - 患者的症状检查器 - （初级）护理提供者的决策支持工具（基本）护理提供者提供指导提供指导的其他检查和转介的研究，以实现较高的诊断，以实现供您的数据诊断，以实现诊断性的诊断学组，并在rheumatient neware distripition中，介绍了介绍性的介绍性，介绍了rheumatic in norigation contriost of rheumatic in nocationic in nocation necaligation necaligation，通过符合GDPR的数字研究环境和联合学习管道进行集成和共享分析。最后，我们将通过利益相关者的研究测试模型的可接受性，并提供针对欧洲当前医疗保健量身定制的实施场景。