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

全球有17.1亿人有肌肉骨骼症状，这是导致残疾的主要原因。早期疾病分层对于确保适当的护理(最合适的医疗保健提供者和最佳治疗选择)很重要。目前，患者到诊断和有效治疗的路程长，效率低，导致持续的疾病负担和经济损失。这是由于对疾病病因的关系和疾病之间症状的相似性认识不够，在最初的治疗中没有充分区分测试和试错方法。SPIDeRR旨在通过考虑影响患者症状的因素的完整网络，来理清风湿病早期诊断的现实复杂性。SPIDeRR的方法将在以下方面远远超出最先进的水平：-通过在具有类似症状的患者中识别不同的疾病组，需要不同的治疗方法，而不是传统的旨在早期捕获一种疾病的方法。-通过整合每个医疗保健级别的所有相关数据维度(初级和二级保健以及在线寻求建议的患者)。-通过将“组学”领域的机器学习技术翻译并应用于临床患者数据，这将为翻译数据科学带来新的管道。SPIDERR将提供三种临床模型--患者症状检查器--为(初级)保健提供者提供指导额外检查和转诊决定的决策支持工具--患者-患者相似性网络，以优化风湿病的诊断群体并支持治疗决策，从而实现这一目标。此外，我们还通过符合GDPR的数字研究环境和联合学习管道，提供数据集成和共享分析的解决方案。最后，我们将通过利益相关者研究测试模型的可接受性，并提供适合欧洲当前医疗保健的实施场景

Globally 1.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 economical loss. This is due to insufficiently understood relations disease causes and similarities in symptoms between diseases, insufficiently distinguishing tests, 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