Deep Patient Trajectory Analysis: Learning from electronic health records

深度患者轨迹分析：从电子健康记录中学习

• 批准号: 2446166
• 金额: --
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2446166
• 关键词: Deep; Patient; Trajectory; Analysis; Learning

Electronic health records (EHR) have recently matured into an enormous source of routinely collected information. Today, a single EHR can contain comprehensive medical histories for millions of patients, logging and collating data collected from all levels of healthcare over the course of decades from multiple healthcare institutions. Such a dataset can contain terabytes of information, with billions of entries recorded in a complex underlying data structure. In the past, classical survival analysis techniques have been used extensively to make predictions about the future of patients based on relatively small quantities of information. In my work, I am investigating how we can leverage much more of the information available in electronic health records, by adapting survival analysis techniques to a deep learning framework, to give healthcare providers more accurate survival distribution predictions and therefore improve their ability to make effective healthcare decisions. The primary aim of this research is to produce deep learning models that are capable of processing a patient's entire medical history and returning a precise distribution for the time to an event of interest. This will require several independent components. Firstly, we will develop a robust method of pre-processing multi-modal, multi-source longitudinal EHR data. Secondly, we will need a suitable deep learning architecture that can identify the potentially complicated patterns in this data to create an accurate model of a patient and their susceptibility to the event of interest. Finally, we want our models to output a suitable survival distribution for the time to the event of interest. While there has already been some research into applying deep learning to EHR data, this methodology is novel in that it goes further than previous work, which has only looked into the probability of events occurring between discrete timepoints. I also plan to extend the work further by adapting more advanced survival analysis techniques to a deep learning framework, by exploring competing risks and multi-state models as well as scoring for distribution family selection. This research is being done in collaboration with the NIHR ARC Northwest London research group and methods developed will be applied to data from the Northwest London Whole System Integrated Care (WSIC) database, with the aim of providing local healthcare providers with improved tools to understand and improve their care. The first case studies we plan to test our methods on include investigating time to events and multi-state modelling for patients suffering from Diabetes, heart-conditions as well as those with multi-morbidities. This project falls within the EPSRC healthcare technologies research area.

电子健康记录 (EHR) 最近已发展成为常规收集信息的巨大来源。如今，一个 EHR 可以包含数百万患者的全面病史，记录和整理数十年来从多个医疗机构从各级医疗保健收集的数据。这样的数据集可以包含 TB 级的信息，在复杂的底层数据结构中记录了数十亿条条目。过去，经典的生存分析技术已被广泛使用，基于相对少量的信息来预测患者的未来。在我的工作中，我正在研究如何通过将生存分析技术适应深度学习框架，利用电子健康记录中的更多可用信息，为医疗保健提供者提供更准确的生存分布预测，从而提高他们做出有效医疗保健决策的能力。这项研究的主要目的是产生深度学习模型，能够处理患者的整个病史并返回感兴趣事件的精确时间分布。这将需要几个独立的组件。首先，我们将开发一种强大的方法来预处理多模式、多源纵向 EHR 数据。其次，我们需要一个合适的深度学习架构，可以识别这些数据中潜在的复杂模式，以创建患者及其对感兴趣事件的易感性的准确模型。最后，我们希望我们的模型能够针对感兴趣的事件输出合适的生存分布。虽然已经有一些将深度学习应用于 EHR 数据的研究，但这种方法的新颖之处在于它比之前的工作更进一步，之前的工作仅研究离散时间点之间发生事件的概率。我还计划通过将更先进的生存分析技术应用于深度学习框架、探索竞争风险和多状态模型以及分布族选择评分来进一步扩展工作。这项研究是与 NIHR ARC 西北伦敦研究小组合作完成的，所开发的方法将应用于伦敦西北部整体系统综合护理 (WSIC) 数据库的数据，旨在为当地医疗保健提供者提供改进的工具来了解和改善他们的护理。我们计划测试我们方法的第一个案例研究包括调查糖尿病、心脏病以及患有多种疾病的患者的事件发生时间和多状态模型。该项目属于 EPSRC 医疗保健技术研究领域。