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

心脏病是导致死亡的主要原因，经常导致心律失常或机械性死亡，如心力衰竭，这是一种心脏停止正常供血的情况。尽管调整生活方式和终生治疗来控制症状，但这种情况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心脏病在不同患者群体中的可变性，并正确评估严重程度和风险。通过提高诊断敏感性、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个体病情的治疗方案，从多模态数据中获得的新生物标志物可以提供比当前技术更强的预后。该项目的目标是开发新的计算方法，利用人工智能方法与机械建模和仿真之间的协同作用，实现精确心脏病学。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，并确定治疗靶点和治疗方法，以最好地改善每个亚组的结果。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，并通过数字配对和模拟来增强这些信息。通过使用机器学习策略将临床数据（如心脏磁共振成像和心电图）与患者人口统计学（如年龄、性别、合并症）和遗传学相结合，确定具有代表性的患者群体。无监督聚类策略可以提供初步的患者亚组。这些子组可以与基于生成模型的新型数据表示策略获得的子组进行比较或增强（Beetz et al., 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群的生物标志物。这些亚组之间的机制差异可以使用心脏数字双胞胎来解释，这些数字双胞胎是使用现有的机电管道构建的，该管道基于获得的患者簇的质心来模拟心脏活动（Camps等人，2021，Banerjee等人，2021）。为了提高临床中的靶标识别，我们将通过对每组代表性数字孪生体进行模拟测试来研究治疗方案的有效性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，并在每个患者的基础上进行心脏模拟，以揭示疾病的机制。这项工作旨在改进目前的诊断方法，为心脏病患者发现更有针对性的治疗方案，帮助减轻致命心脏病的全球负担。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领域，旨在解决优化疾病预测、诊断和干预的重大挑战。

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.