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

纵向电子健康记录的端到端概率建模

• 批准号: 2247906
• 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: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2247906
• 关键词: End; Probabilistic; Modelling; Longitudinal; Electronic

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.

最近大量电子健康记录（EHR）数据集的可用性导致了医疗保健文献中机器学习的激增。本论文旨在建立一个桥梁之间的新提出的方法和经典文献的概率建模。因此，它特别关注医学数据的典型属性：（1）广泛缺失，（2）不规则间隔的观察，（2）模型解释和（3）半监督。在第一步中，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）来估算缺失数据。我们提供了一种直觉，为什么这些深度生成插补方法的工作，并使其有可能编码到模型中的缺失数据的先验信息。在第二步中，我们处理不规则间隔的观察，通过使用时间连续神经网络建模潜在的分类变量。因此，我们允许对潜在空间中的时间条件概率进行建模。第三步，我们开发了可解释的方法，根据观察到的实验室值和调查答案揭示疾病的潜在动态。这些潜在的动力学可以在临床试验中用作疗效指标，以评估药物是否有效。可应用于此问题的常见潜在因素模型通常仅限于捕获线性相互作用。然而，时间连续的非线性模型无法给出可解释的结果。我们的目标是找到一个稀疏的线性近似的非线性动力学发现的连续深潜模型。在第四步中，我们想探索如何通过包括半监督来控制这样一个深度潜在模型。在临床试验中，现有疗效指标可用于某些临床访视。我们的目标是找到一种疾病的新的疗效措施，不遭受现有措施的天真，但在某种程度上与现有的措施正相关。另一个模型旨在解释可能的偏差。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评估了所提出的方法，例如医学基准数据集MIMIC III和诺华公司提供的临床试验数据集。我的研究部分由诺华公司资助。我的上司是克里斯·霍姆斯。该项目属于EPSRC医疗保健技术研究领域的福尔斯。