Deep clinical trajectory modeling to optimize accrual to cancer clinical trials

深度临床轨迹建模可优化癌症临床试验的应计结果

基本信息

批准号：
10561692
负责人：
Kenneth L Kehl
金额：
$ 24.9万
依托单位：
DANA-FARBER CANCER INST
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-02-01 至 2025-01-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10561692
关键词：
Academia Adult Cancer Model Cancer Patient Classification Clinical Clinical Data Clinical Medicine Clinical Trials Complex Computers Dana-Farber Cancer Institute Data Data Science Data Scientist Development Diagnosis Disease Electronic Health Record Eligibility Determination Enrollment Goals Government Health Services Research Health system Healthcare Systems Histology Industry Institution Intervention Label Link Machine Learning Malignant Neoplasms Manuals Medical Records Methods Modeling Natural Language Processing Oncologist Oncology Outcome Pathology Report Patients Phenotype Primary Neoplasm Radiology Specialty Randomized Reporting Research Research Personnel Resources Services Site Source Structure Systemic Therapy Techniques Technology Text Therapeutic Trials Time Training Work anticancer research burden of illness cancer care cancer clinical trial care delivery career clinical candidate clinical practice clinical training clinical trial enrollment clinical trial protocol clinically relevant cohort data registry deep learning design electronic health data genomic data improved innovation learning strategy machine learning model multiple data types neoplasm registry neural network architecture novel palliative patient population precision medicine clinical trials prevent programs response skills structured data survival prediction tool transfer learning trial readiness tumor progression unstructured data

项目摘要

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.

项目概要/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。海量数据电子病历所包含的内容可能构成研究和质量改进的重要资源，但迄今为止，电子病历尚未完全实现这一潜力。重要的临床终点，例如疾病组织学、分期、反应、进展和负担通常仅以非结构化自由文本形式记录在 EHR 中。甚至当结构化数据可用时，它们可能仅在某一时间点记录，例如诊断，以及以后在患者的动态疾病轨迹中可能不再那么相关。这些障碍阻碍了可扩展的分析用于相对简单的研究任务的 EHR 数据，例如识别一组患者可能有资格进行临床试验。确定试验患者是癌症研究中的一个重要挑战，因为历史上只有不到 5% 的成人癌症患者参加过治疗试验。工具正在开发中更好地将患者与试验相匹配，但没有这样的工具既是公开可用的，又能够纳入时间- 使用非结构化 EHR 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颖的解决方案。在持续的工作中，我发现自然基于神经网络架构的语言处理可以可靠地提取临床相关的肿瘤学信息来自自由文本放射学报告的终点。我的目标是开发一个独立的研究项目，重点关注利用此类方法大规模使用 EHR 来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) 将迁移学习应用于预先训练的癌症轨迹模型，使用 EHR 数据和临床试验将患者与临床试验相匹配协议； (3) 试点将癌症轨迹模型纳入机构临床试验匹配工具。在短期内，这项工作将促进我们机构的临床试验的进展。期间该提案的独立研究部分，它将构成开展总体框架的基础使用 EHR 数据进行可扩展的癌症研究。