(Renewal) I-AIM: Individualised Artificial Intelligence for Medicine

管理和治疗复杂的慢性疾病，如阿尔茨海默病是现代医学面临的最大挑战之一。自2003年以来，所有减缓或阻止阿尔茨海默病进展的临床试验都失败了。这可能是由于阿尔茨海默病的复杂性和持续时间（数十年），加上疾病及其进展的高度个体化性质。结合起来，这对临床试验不利，因为很难识别和招募大量处于同一轨迹的同一阶段的个体，因此谁可能从潜在的治疗中受益。原则上，这一挑战可以通过一套称为数据驱动疾病进展建模（D3 PM）的现代计算方法来应对，但首先需要一些技术发展。D3 PM旨在将联合收割机统计数据与人工智能和数据科学的最新发展相结合，以估计描述疾病进展的疾病特征。这个活跃的研究领域从基本的监督机器学习（模式学习/识别）发展到一系列现象学（自上而下）模型和机械（自下而上）模型，这些模型包含了一系列人工智能工具，包括无监督机器学习（模式发现）。D3 PM信号在神经退行性疾病（如阿尔茨海默病）的严重程度评估和进展预测方面显示出了希望，但目前它们缺乏个体水平的精确性和机制严谨性。该研究和创新项目是技术开发和转化产品开发的独特组合：一系列用于个体化D3 PM和扩展机制建模的新技术开发;以及基于这种下一代技术开发药物开发工具的转化努力。结合起来，这项工作将通过提高临床试验的效率来加速药物开发：招募合适个体的较小队列将降低成本，并导致更少的假阴性结果-药物对一小部分人群有效，但试验无法检测到它，因为大多数人对治疗没有反应。选择的应用是阿尔茨海默氏症，但这些想法适用于类似的复杂，进行性疾病。这个奖学金是我职业生涯的重要发射台，更新是实现我的目标必不可少的。我的目标是通过为复杂的医疗挑战提供强大的计算解决方案来造福患者和社会。我实现这一目标的愿景始于针对全球流行的痴呆症，我已经确定了一个未满足的需求（改善临床试验），并以这项研究和创新项目的形式提出了一个可行的解决方案。奖学金的更新提供了必要的资源，以利用我最近在该领域的进展，并个人发展成为一个英国的未来领导者在使用人工智能的医疗和健康。

Management and treatment of complex, chronic diseases such as Alzheimer's disease is one of the biggest challenges facing modern medicine. All clinical trials of investigational treatments for slowing or stopping the progression of Alzheimer's disease since 2003 have failed. This is likely due to the complexity and duration (decades) of Alzheimer's disease, coupled with the highly individual nature of the disease and its progression. Combined, this works against clinical trials by making it extremely difficult to identify and recruit a large group of individuals who are at the same stage of the same trajectory, and so who might benefit from a potential treatment. In principle, this challenge can be met by a set of modern computational approaches called data-driven disease progression modelling (D3PM), but some technological development is required first.D3PM aims to combine statistics with the latest developments in AI and data science to estimate disease signatures that describe how a progressive disease plays out from beginning to end. This active research field grew from basic supervised machine learning (pattern learning/recognition) to a range of phenomenological (top-down) models, and mechanistic (bottom-up) models that incorporate a range of AI tools including unsupervised machine learning (pattern discovery). D3PM signatures have shown promise for estimating severity and predicting progression in neurodegenerative diseases such as Alzheimer's disease, but they currently lack in individual level precision, and mechanistic rigour.This research and innovation project is a unique combination of technology development and translational product development: a series of novel technological developments for individualising D3PM and expanding mechanistic modelling; and translational efforts to develop drug-development tools based on this next-generation technology. In combination, this work will speed up drug-development by increasing the efficiency of clinical trials: recruiting smaller cohorts of suitable individuals will reduce costs and lead to fewer false-negative results - where a drug works on a fraction of the population, but the trial cannot detect it because the majority did not respond to treatment. The chosen application is Alzheimer's disease, but the ideas are fit-for-purpose for similarly complex, progressive diseases.This fellowship is a significant launchpad for my career and the renewal is essential to realise my goals. My ambition is to benefit patients and society by providing robust computational solutions to complex healthcare challenges. My vision for achieving this ambition starts by targeting the global epidemic of dementia, where I have identified an unmet need (improving clinical trials) and proposed a viable solution in the form of this research and innovation project. The fellowship renewal provides essential resources to capitalise on my recent progress in the field and to personally develop into a UK-based future leader in using AI for medicine and health.