Advancing machine learning to achieve real-world early detection and personalised disease outcome prediction of inflammatory arthritis

推进机器学习以实现炎症性关节炎的真实早期检测和个性化疾病结果预测

• 批准号: EP/Y019393/1
• 负责人: Weizi Li
• 金额: $ 78.96万
• 依托单位: University of Reading
• 依托单位国家: 英国
• 项目类别: Research Grant
• 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=EP%2FY019393%2F1
• 关键词: Advancing; machine; learning; achieve; real

Over 20 million people in the UK live with rheumatic and musculoskeletal diseases (RMD), and inflammatory arthritis (IA) is a major subdivision of RMD causing joint inflammation leading to damage. IA causes long-term pain, disability and incurs substantial personal and societal costs. There is also an estimated 59% increase in diagnosed IA cases between 2004 and 2020 in the UK which has important implications for health services. Rheumatology departments accounted for approximately 9% of the average NHS trusts total medication spend in 2019/2020. There are still significant unmet needs in the IA patient pathway, especially in IA detection and flare management. IA presents with non-specific symptoms and there is currently no diagnostically definitive single biomarker for IA. Early detection is critical but challenging, and delay in detection and late referral often result in loss of the window of opportunity when effective treatment should start and delays can lead to disability and associated unemployment. For patients who are diagnosed with IA, IA outcomes and activities such as flare-up are very heterogeneous in their manifestations between individual patients. Real-world data from The National Early Inflammatory Arthritis Audit showed inequality in care for rheumatology patients from minority ethnic groups. A lower proportion of ethnic minority patients achieved disease remission compared to white patients. UN4 Finally, weather is another contributing factor of IA flare heterogeneity. Despite significant unmet needs, RMD, especially IA, is still an underexplored area of real-world ML application in comparison with other diseases. Existing ML studies do not fit for purpose of early detection in practice as they are not trained based on the data available at the point of early detection. Furthermore, although there are studies showing potential determinants of IA, there is no research, or any machine learning methods that can identify the undetected determinants-combination that can offer a useful level of prediction of IA. This is because current ML approaches still cannot handle the underlying relationships among heterogenous datasets with different data types, modalities, contexts, cohorts and levels of incompleteness. On the other hand, existing ML methods in IA, and healthcare in general, still rely on a "one-size-fits-all" paradigm rendering generic learning algorithms, suboptimal on the individual level especially as IA is known to be heterogenous in nature from the time of diagnosis. Although there are methods for explainable ML local, there is limited research to quantify and explain model prediction uncertainty and its usability in practice. For a physician to use and trust ML predictions it is critical to understand the uncertainty associated with these predictions for the individual patient. Although successful translation requires bringing together expertise and stakeholders from many disciplines, the development of ML solutions is currently occurring in silos, and there is a lack of holistic and scalable ML development pipeline. Despite all the limitations of current ML, there are huge opportunities to advance ML, especially in rheumatology applications, because rheumatology has already been leading the way in the use of virtual clinics and remote monitoring in the UK. It is now time to advance ML using data generated for real early detection and personalised management of IA. Our vision: The proposed project will develop useful and responsible machine learning methods to achieve real-world early detection and personalised disease outcome prediction of inflammatory arthritis. We will develop a holistic and scalable approach through an interdisciplinary team addressing the pressing healthcare challenges of inflammatory arthritis and the limitations of machine learning to accelerate real-world ML application in healthcare.

英国有超过 2000 万人患有风湿性和肌肉骨骼疾病 (RMD)，而炎症性关节炎 (IA) 是 RMD 的一个主要分支，会导致关节炎症并导致损伤。 IA 会导致长期疼痛、残疾，并造成巨大的个人和社会成本。据估计，2004 年至 2020 年间，英国确诊的 IA 病例增加了 59%，这对卫生服务产生了重要影响。 2019/2020 年，风湿病科约占 NHS 信任总药物支出的平均 9%。 IA 患者途径中仍然存在大量未满足的需求，特别是在 IA 检测和耀斑管理方面。 IA 表现为非特异性症状，目前还没有诊断明确的 IA 单一生物标志物。早期发现至关重要但具有挑战性，延迟发现和延迟转诊往往会导致失去应该开始有效治疗的机会，而延迟可能导致残疾和相关失业。对于被诊断为 IA 的患者，IA 的结果和活动（例如突发）在个体患者之间的表现非常不同。来自国家早期炎症性关节炎审计的真实世界数据显示，少数民族风湿病患者的护理存在不平等。与白人患者相比，少数族裔患者获得疾病缓解的比例较低。 UN4 最后，天气是 IA 耀斑异质性的另一个影响因素。尽管存在大量未满足的需求，但与其他疾病相比，RMD，尤其是 IA，仍然是现实世界中 ML 应用的一个尚未开发的领域。现有的机器学习研究不适合实践中早期检测的目的，因为它们没有根据早期检测时可用的数据进行训练。此外，尽管有研究表明 IA 的潜在决定因素，但没有研究或任何机器学习方法可以识别未检测到的决定因素组合，从而提供有用的 IA 预测水平。这是因为当前的机器学习方法仍然无法处理具有不同数据类型、模式、上下文、群组和不完整性级别的异构数据集之间的潜在关系。另一方面，IA 和一般医疗保健中现有的 ML 方法仍然依赖于“一刀切”的范式来呈现通用学习算法，这在个人层面上并不是最优的，尤其是因为众所周知 IA 本质上是异构的从诊断时起。尽管有可解释的机器学习局部方法，但量化和解释模型预测不确定性及其在实践中的可用性的研究有限。对于使用和信任 ML 预测的医生来说，了解与单个患者的这些预测相关的不确定性至关重要。尽管成功的翻译需要汇集来自多个学科的专业知识和利益相关者，但机器学习解决方案的开发目前是在孤岛中进行的，并且缺乏整体和可扩展的机器学习开发管道。尽管当前的机器学习存在种种局限性，但推进机器学习仍有巨大的机会，特别是在风湿病学应用中，因为风湿病学在英国的虚拟诊所和远程监控的使用方面已经处于领先地位。现在是时候利用生成的数据来推进机器学习，以实现 IA 的真正早期检测和个性化管理。我们的愿景：拟议的项目将开发有用且负责任的机器学习方法，以实现炎症性关节炎的真实早期检测和个性化疾病结果预测。我们将通过跨学科团队开发一种整体且可扩展的方法，解决炎症性关节炎所面临的紧迫的医疗保健挑战和机器学习的局限性，以加速现实世界中的机器学习在医疗保健中的应用。

项目成果

Identifying Key Health System Components Associated with Improved Outcomes to Inform the Re-Configuration of Services for Adults with Rare Autoimmune Rheumatic Diseases: A Mixed Methods Study

确定与改善结果相关的关键卫生系统组成部分，为罕见自身免疫性风湿病成人服务的重新配置提供信息：一项混合方法研究

• DOI: 10.2139/ssrn.4687145
• 发表时间: 2024
• 作者: Hollick R

A concept for digital transformation for improved patient care in the UK

改善英国患者护理的数字化转型概念

• DOI: 10.1016/j.hlpt.2023.100775
• 发表时间: 2023
• 期刊: Health Policy and Technology
• 影响因子: 6
• 作者: Chan A

Improving triaging from primary care into secondary care using heterogeneous data-driven hybrid machine learning

• DOI: 10.1016/j.dss.2022.113899
• 发表时间: 2023-01-31
• 期刊: DECISION SUPPORT SYSTEMS
• 影响因子: 7.5
• 作者: Wang，Bing;Li，Weizi;Chan，Antoni T. Y.
• 通讯作者: Chan，Antoni T. Y.

Artificial intelligence and machine learning in rheumatology

风湿病学中的人工智能和机器学习

• DOI: 10.1093/rheumatology/keae092
• 发表时间: 2024
• 期刊: Rheumatology
• 影响因子: 5.5
• 作者: Dubey S

Early detection of inflammatory arthritis to improve referrals using multimodal machine learning from blood testing, semi-structured and unstructured patient records

利用血液检测、半结构化和非结构化患者记录中的多模式机器学习，早期发现炎症性关节炎，以改善转诊

• DOI: 10.48550/arxiv.2310.19967
• 发表时间: 2023
• 作者: Wang B