Deep clinical trajectory modeling to optimize accrual to cancer clinical trials

项目摘要/摘要 电子健康记录(EHR)现在在常规癌症护理服务中无处不在。海量数据 EHR所包含的内容可以构成研究和质量改进的重要资源，但到目前为止， EHR还没有完全实现这一潜力。重要的临床终点，如疾病组织学、分期、 回应、进展和负担通常只以非结构化的自由文本形式记录在EHR中。连 当结构化数据可用时，它们可能只在一个时间点被记录，例如诊断，以及 在患者的动态疾病轨迹中，可能不是那么相关。这些障碍阻碍了可伸缩分析 用于甚至相对简单的研究任务的电子病历数据，例如识别一群患者 可能有资格进行临床试验。在癌症研究中，确定患者进行试验是一个重要的挑战， 因为历史上不到5%的成年癌症患者参加了治疗试验。工具正在开发中，以 更好地将患者与试验相匹配，但没有这样的工具既公开可用，又能够结合时间- 使用非结构化电子病历数据生成的特定患者表型。最近深度学习领域的快速创新 技术可以为这些挑战提供新的解决方案。在正在进行的工作中，我发现这很自然 基于神经网络结构的语言处理可以可靠地提取与临床相关的肿瘤学 来自自由文本放射学报告的端点。我的目标是开发一个独立的研究项目，专注于 利用这些方法将EHR投入大规模使用，用于发现和改善癌症护理服务。我的 具体目标是(1)开发和验证临床相关的、动态的、预先训练的癌症轨迹模型 通过将深度学习应用于集成的结构化和非结构化EHR数据；(2)将迁移学习应用于 使用EHR数据和临床试验将患者与临床试验相匹配的预先训练的癌症轨迹模型 方案；以及(3)将癌症轨迹建模纳入机构临床试验的试点 匹配工具。在短期内，这项工作将促进我们机构临床试验的收益。在.期间 独立研究部分的提案，它将构成开展总体框架的基础 使用电子病历数据进行可扩展的癌症研究。

PROJECT SUMMARY/ABSTRACT Electronic health records (EHRs) are now ubiquitous in routine cancer care delivery. The large volumes of data that EHRs contain could constitute an important resource for research and quality improvement, but to date, EHRs have not fully realized this potential. Important clinical endpoints, such as disease histology, stage, response, progression, and burden, are often recorded in the EHR only in unstructured free-text form. Even when structured data are available, they may be recorded only at one point in time, such as diagnosis, and may not be as relevant later in a patient's dynamic disease trajectory. These barriers prevent scalable analysis of EHR data for even relatively straightforward research tasks, such as identification of a cohort of patients potentially eligible for clinical trials. Identifying patients for trials is an important challenge in cancer research, since under 5% of adults with cancer have historically enrolled in therapeutic trials. Tools are in development to better match patients to trials, but no such tools are both publicly available and capable of incorporating time- specific patient phenotypes generated using unstructured EHR data. Recent rapid innovation in deep learning techniques could provide novel solutions to these challenges. In ongoing work, I have found that natural language processing based on a neural network architecture can reliably extract clinically relevant oncologic endpoints from free-text radiology reports. My goal is to develop an independent research program focused on leveraging such methods to put the EHR to use at scale for discovery and improving cancer care delivery. My specific aims are (1) to develop and validate a clinically relevant, dynamic, pre-trained cancer trajectory model by applying deep learning to integrated structured and unstructured EHR data; (2) to apply transfer learning to a pre-trained cancer trajectory model to match patients to clinical trials using EHR data and clinical trial protocols; and (3) to pilot the incorporation of cancer trajectory modeling into an institutional clinical trial matching tool. In the near term, this work will facilitate accrual to clinical trials at our institution. During the independent research portion of the proposal, it will constitute the basis for a general framework for conducting scalable cancer research using EHR data.