Dissecting disease heterogeneity in cardiac patients using multimodal machine learning, modelling, and simulation method

使用多模式机器学习、建模和模拟方法剖析心脏病患者的疾病异质性

• 批准号: 2592417
• 金额: --
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2592417
• 关键词: Dissecting; disease; heterogeneity; cardiac; patients

Cardiac disease is a major cause of mortality, often leading to arrhythmic or mechanical death such as in heart failure, a condition which occurs when the heart stops being able to pump blood correctly. Despite lifestyle adjustments and life-long treatment to manage symptoms, the condition typically worsens over time and can lead to death or an urgent heart transplant. New methods are required to account for the variability of cardiac disease across a diverse patient population and to correctly evaluate severity and risks. New biomarkers derived from multimodal data could provide a stronger prognosis than current techniques by increasing diagnostic sensitivity, providing more details on the pathophysiology, predicting disease progression, and identifying the therapy option that best mitigates each individual's condition.The objective of this project is to develop novel computational methods that exploit the synergy between AI approaches and mechanistic modelling and simulation for the realisation of precision cardiology. Specific goals include identifying new multimodal biomarkers to define cardiac disease subgroups, investigating the underlying mechanisms of disease that explain these subgroups, and identifying therapeutic targets and treatments that best improve the outcomes of each subgroup. These studies will be carried out by using data-driven methods to leverage the wealth of information contained in the UK Biobank and Clinical Practice Research Datalink electronic health records, and by augmenting this information through digital twinning and simulations. Representative groups of patients will be identified by using machine learning strategies to integrate clinical data, such as cardiac magnetic resonance imaging and electrocardiogram, with patient demographics (e.g. age, sex, co-morbidities) and genetics. Unsupervised clustering strategies could provide preliminary patient subgroups. These subgroups could be compared to, or enhanced by, subgroups obtained from novel data representation strategies based on generative models (Beetz et al., 2022). This would help explain the phenotypical variability of cardiac disease in the human population and help automatically identify the biomarkers that define subgroups. Mechanistic differences between these subgroups can be explained using cardiac digital twins, which are built using an existing electromechanical pipeline that simulates cardiac activity based on the centroids of patient clusters obtained (Camps et al., 2021, Banerjee et al., 2021). In order to improve target identification in the clinic, we will investigate the efficacy of therapeutic options and their link to the new biomarkers by using simulation testing on each group's representative digital twin.In summary, the project will deliver novel strategies combining AI and simulation methods, helping to uncover new data-driven trends in cardiac biomarkers and perform cardiac simulations on a per-patient basis level to unravel mechanisms of disease. The work aims to improve current diagnosis methods and enable the discovery of more tailored treatment regimens for cardiac patients, helping to alleviate the global burden of fatal cardiac diseases.This project falls within the EPSRC research area of Healthcare Technologies, more specifically at the intersection of Clinical Technologies and Analytical Science, and addresses the Grand Challenge of optimising disease prediction, diagnosis and intervention.

心脏病是死亡的主要原因，通常导致心律失常或机械性死亡，例如心力衰竭，当心脏无法正常泵血时就会发生这种情况。尽管调整生活方式和终生治疗来控制症状，但病情通常会随着时间的推移而恶化，并可能导致死亡或紧急心脏移植。需要新的方法来解释不同患者群体中心脏病的变异性，并正确评估严重程度和风险。来自多模态数据的新生物标志物可以通过提高诊断灵敏度、提供更多病理生理学细节、预测疾病进展以及确定最能缓解每个人病情的治疗方案，提供比现有技术更强的预后。该项目的目标是开发新颖的计算方法，利用人工智能方法与机械建模和模拟之间的协同作用，以实现精准心脏病学。具体目标包括确定新的多模式生物标志物来定义心脏病亚组，研究解释这些亚组的疾病的潜在机制，以及确定最能改善每个亚组结果的治疗目标和治疗方法。这些研究将通过使用数据驱动的方法来利用英国生物银行和临床实践研究数据链电子健康记录中包含的丰富信息，并通过数字孪生和模拟来增强这些信息。将通过使用机器学习策略将临床数据（例如心脏磁共振成像和心电图）与患者人口统计数据（例如年龄、性别、合并症）和遗传学相结合来确定代表性患者群体。无监督聚类策略可以提供初步的患者亚组。这些子组可以与从基于生成模型的新颖数据表示策略获得的子组进行比较或增强（Beetz 等人，2022）。这将有助于解释人群中心脏病的表型变异性，并有助于自动识别定义亚组的生物标志物。这些亚组之间的机制差异可以使用心脏数字孪生来解释，心脏数字孪生是使用现有的机电管道构建的，该管道根据获得的患者簇的质心模拟心脏活动（Camps 等人，2021；Banerjee 等人，2021）。为了改善临床目标识别，我们将通过对每个组的代表性数字双胞胎进行模拟测试，研究治疗方案的功效及其与新生物标志物的联系。总之，该项目将提供结合人工智能和模拟方法的新颖策略，帮助发现心脏生物标志物的新数据驱动趋势，并在每个患者的水平上进行心脏模拟，以揭示疾病机制。这项工作旨在改进当前的诊断方法，并为心脏病患者找到更适合的治疗方案，帮助减轻全球致命心脏病的负担。该项目属于 EPSRC 医疗技术研究领域，更具体地说，是临床技术和分析科学的交叉点，解决优化疾病预测、诊断和干预的巨大挑战。