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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：
10547984
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/10547984
关键词：
Academia Adult Cancer Model Cancer Patient Classification Clinical Clinical Data Clinical Medicine Clinical Trials Clinical trial protocol document 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 base burden of illness cancer care cancer clinical trial care delivery career clinical candidate clinical practice clinical trial enrollment clinically relevant cohort data registry deep learning design electronic 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 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.

项目总结/文摘