Metalearning for Health AI

“学习如何学习”的新人工智能方法尚处于起步阶段；也就是说，在使用庞大的医疗数据集训练非常复杂的模型时，需要改进学习过程。医疗保健人工智能的特点是可以访问非常大的，大多数是“未标记”的数据集，从某种意义上说，要求领域专家标记数据是不可行的——这与传统的人工智能领域（通常是成像）形成鲜明对比，在传统的人工智能领域，标签可以通过众包或其他非专家参与的方式获得。医疗保健时间序列非常复杂，通常存在的唯一标签是“硬终点”，例如患者的最终死亡——这种缺乏标签的原因是，即使对于临床专家来说，标记的临床时间序列（超过100个变量）也不是微不足道的。例如，临床合作者发现很难描述疾病轨迹的许多可能表现，因为在这个具有挑战性的领域中存在着相当大的患者之间和患者内部的可变性。使标签问题进一步恶化的是，一个特定的应用程序可能往往侧重于收集到很少数据的疾病或患者群体。患者可能正在愈合，或试图对治疗产生反应，这一事实进一步增加了标签问题的复杂性。由于上述限制，现有的无监督/半监督/主动学习方法通常表现不佳。虽然迁移学习在许多成像问题中已被证明是成功的（因为传统的深度学习方法可以通过访问来自较大成像任务的数据来显着提高小型目标任务的性能），但由于患者时间序列数据的复杂性和可变性，它通常在非成像医疗保健人工智能领域失败。博士课程的这一研究主题将提出新颖的“无神谕”主动学习方法，认识到临床现实，即标签要么完全缺失，要么充其量，临床医生只能对一小部分患者的时间序列数据给出意见，通常是对可用变量总数的一小部分。基于我们过去的课程学习经验（ICML 2020，其中“学生”复杂网络委员会由“教师”网络监督），我们将提出学生-教师-校长的方法。这种层次结构对应于学生学习所需的临床任务，而教师学习学生潜在表征的可转移摘要。（这将首先使用学生网络的大量多元状态作为教师的输入，从中学习学生的潜在表征。）“主体”是层次结构的最高级别，它将监督这些潜在摘要在医疗保健任务之间的传输，目的是允许我们同步利用大型公开可用和患者机密数据集的价值。这是医疗人工智能的一个关键新方向，通常每个新数据集都需要从头开始。有了这样的新方法，具有相似动态的任务（不一定来自类似的生理条件或习得模式）可以自动识别，校长将鼓励接受过类似动态任务培训的教师之间的学习。其次，数据集归医院网络所有；虽然我们已经获得了每家医院的数据，但这通常只适用于构建原理证明模型。为了使人工智能在全国范围内产生真正的影响，必须开发以联合方式操作数据的技术，在整个“中心”和各种“辐条”之间传输模型/模型更新。该项目属于EPSRC数字经济、医疗保健技术和ICT研究领域。合作。

In its infancy is the development of new AI methods for "learning how to learn"; that is, improving the learning process involved in training very complex models using huge healthcare datasets. Healthcare AI is characterised by having access to very large, mostly "unlabelled" datasets, in the sense that it is infeasible to ask a domain expert to label data - this contrasts with traditional areas of AI (typically imaging), in which labels can be obtained from, for example, crowdsourcing or other means of engaging non-experts. The complexity of healthcare time series are such that often the only labels that exist are "hard end-points", such as the eventual death of a patient - this lack of labels occurs because labelled clinical time series (upwards of 100-variate) is not at all trivial, even for a clinical expert. For example, clinical collaborators find it difficult to characterise the many possible manifestations of disease trajectory, because of the considerable inter- and intra-patient variability that exists in this challenging domain. Further exacerbating the labelling problem is that a particular application may often focus on a disease or a patient group for which very little data have been collected. The fact that a patient may be healing, or attempting to respond to treatment, adds yet further complexity to the labelling problem. Existing methods of unsupervised / semi-supervised / active learning typically perform poorly as a result of the above limitations. While transfer learning has proven successful in many imaging problems (because conventional deep learning methods can boost performance significantly on a small, target task by having access to data from a larger imaging task), it typically fails in the non-imaging healthcare AI domain, due to the complexity and variability of patient time-series data.This theme of research within the doctoral programme will propose novel "oracle-free" methods of active learning, recognising the clinical reality that labels are either entirely absent or that, at best, a clinician can only give an opinion on time-series data form a very small subset of patients, and typically for a small subset of the total number of variables available. Building on our past experience of curriculum learning (ICML 2020, in which committees of "student" complex networks are overseen by "teacher" networks), we will propose student-teacher-principal approaches. This hierarchy corresponds to students learning the required clinical task, while teachers learn transferable summaries of latent representations of the students. (This will initially use the massively-multivariate state of the student networks as input to the teachers, from which latent representations of the students will be learned.) The "principal" is the highest level of the hierarchy, which will oversee the transfer of these latent summaries between healthcare tasks, with the aim of allowing us to exploit the value of large publicly-available and patient-confidential datasets in synchrony. This is a critical new direction for Health AI, where typically every new dataset involves "starting again" ab initio. With such new methods, tasks with similar dynamics (not necessarily from analogous physiological conditions or acquisition modalities) can be identified automatically, and where the principal will encourage learning across teachers trained on tasks of similar dynamics.Secondly, datasets are owned by hospital networks; while we have gained access to data on a hospital-by-hospital basis, this is often only appropriate for constructing proof-of-principle models. For AI to make a real impact at national scale, technologies must be developed for operating on data in a federated manner, transmitting models / model updates between a "hub" and the various "spokes" throughout This project falls within the EPSRC Digital Economies, Healthcare Technologies, and ICT research areas. collaborations.