End-to-End Probabilistic Modelling of Longitudinal Electronic Health Records

最近电子健康记录(EHR)大数据集的出现导致了医疗保健文献的机器学习的激增。本论文的目的是在新提出的方法和概率建模的经典文献之间架起一座桥梁。因此，它特别关注医学数据的典型属性：(1)广泛的缺失性，(2)不规则间隔的观测，(2)模型解释和(3)半监督。在第一步，我们探索如何使用深度生成模型(生成新数据的非线性模型)来估计缺失数据。在第二步中，我们通过使用时间连续神经网络对潜在的分类变量进行建模来处理不规则间隔的观测。因此，我们允许在潜在空间中对时间条件概率进行建模。在第三步中，我们开发了可解释的方法，基于观察到的实验室值和调查答案来揭示疾病的潜在动态。然后，这些潜在的动态可以在临床试验中用作疗效衡量标准，以评估药物是否有效。可以应用于这一问题的常见潜在因素模型通常被限制在捕捉线性相互作用。然而，时间连续的非线性模型不能给出可解释的结果。我们的目标是找到一个稀疏线性近似的非线性动力学发现连续的深潜伏期模型。在第四步，我们希望探索如何通过纳入半监管来控制这样一个深层次的潜在模式。在临床试验中，现有的疗效措施可用于一些临床访问。其目标是找到一种疾病的新疗效指标，既不受现有措施的幼稚影响，又在某种程度上与现有措施呈正相关。另一个模型旨在解释可能的偏差。然后，它应该确保偏差是合理的。为了证明我们模型的有效性，我们在各种真实世界的医学数据集上对所提出的方法进行了评估，例如医学基准数据集Mimic III和诺华提供的临床试验数据集。我的研究部分由诺华公司资助。我的主管是克里斯·福尔摩斯。该项目属于EPSRC医疗保健技术研究领域。

The recent availability of large data sets of electronic health records (EHR) led to a surge in the machine learning for health care literature. This thesis aims to build a bridge between the newly proposed methods and the classical literature on probabilistic modelling. It thereby pays special attention to typical properties of medical data: (1) extensive missingness, (2) irregularly spaced observations, (2) model interpretation and (3) semi-supervision.In a first step, we explore how deep generative models (non-linear models that generate new data) can be used to impute missing data. We provide an intuition why some of these deep generative imputation methods work and make it possible to encode prior information on the missing data into the model.In a second step, we deal with irregularly spaced observations by modelling latent categorical variables using in-time-continuous neural networks. We hereby allow to model time conditional probabilities in the latent space. Such a model can then be used to cluster the disease states of a patient in time.In a third step, we develop interpretable methodology that reveals the latent dynamics of a disease based on observed lab values and survey answers. These latent dynamics can then be used as efficacy measures in clinical trials to assess whether a drug is working or not. Common latent factor models that can be applied to this problem are often restricted to capture linear interactions. Time-continuous nonlinear models however fail in giving interpretable results. Our goal is to find a sparse linear approximation to the non-linear dynamics found by continuous deep latent models. In a fourth step, we want to explore how such a deep latent model could be controlled by including semi-supervision. In clinical trials, existing efficacy measures are available for some of the clinical visits. The goal is to find novel efficacy measures of a disease that do not suffer from the naivety of the existing measures but are to some degree positively correlated with the existing measures. An additional model is aimed to explain possible deviations. It should then ensure that the deviations are reasonable.To demonstrate the effectiveness of our models, we evaluate the proposed methodology on various real-world medical datasets such as the medical benchmark data set MIMIC III and clinical trial data sets provided by Novartis.My research is partly funded by Novartis. My supervisor is Chris Holmes. This project falls within the EPSRC Healthcare technologies research area.