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

• 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
• 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

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.

项目摘要/摘要 现在，电子健康记录（EHR）在常规癌症护理中无处不在。大量数据 EHR所包含的可能构成研究和质量改进的重要资源，但迄今为止 EHR尚未完全意识到这一潜力。重要的临床终点，例如疾病组织学，阶段， 反应，进步和负担通常仅以非结构化的自由文本形式记录在EHR中。甚至 当有结构化的数据可用时，它们只能在一个时间点记录，例如诊断和 在患者的动态疾病轨迹中可能不那么相关。这些障碍阻止可扩展分析 EHR数据甚至相对简单的研究任务，例如识别一系列患者 可能有资格参加临床试验。识别患者进行试验是癌症研究中的重要挑战 由于5％以下的癌症成年人历史上已经参加了治疗试验。工具正在开发中 更好地将患者与试验相匹配，但是没有这样的工具可以公开使用，并且能够纳入时间 - 使用非结构化EHR数据生成的特定患者表型。深度学习的最新快速创新 技术可以为这些挑战提供新颖的解决方案。在正在进行的工作中，我发现很自然 基于神经网络体系结构的语言处理可以可靠地提取临床上相关的肿瘤学 自由文本放射学报告的终点。我的目标是制定一个专注于 利用此类方法使EHR大规模使用进行发现和改善癌症护理的分娩。我的 具体目的是（1）开发和验证临床相关，动态，预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的EHR数据； （2）将转移学习应用于 使用EHR数据和临床试验将患者与临床试验相匹配的预训练的癌症轨迹模型 协议； （3）试行将癌症轨迹建模纳入机构临床试验 匹配工具。在短期内，这项工作将有助于我们机构的临床试验。在 该提案的独立研究部分将构成进行一般框架的基础 使用EHR数据可扩展的癌症研究。