Cancer Deep Phenotype Extraction from Electronic Medical Records

从电子病历中提取癌症深层表型

基本信息

批准号：
9298609
负责人：
Rebecca S Jacobson
金额：
$ 18.78万
依托单位：
UNIVERSITY OF PITTSBURGH AT PITTSBURGH
依托单位国家：
美国
项目类别：
财政年份：
2014
资助国家：
美国
起止时间：
2014-05-06 至 2017-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9298609
关键词：
Advanced Development Advanced Malignant Neoplasm Apache Automobile Driving Behavior Boston Cancer Biology Cancer Patient Characteristics Clinical Clinical Data Collaborations Communities Computer software Computerized Medical Record Data Development Diagnosis Disease Ensure Epigenetic Process Etiology Evaluation Funding Gene Amplification Gene Proteins General Qualifier Genetic Genomics Goals Heterogeneity Imagery Immune System Diseases Individual Informatics Investigation Laboratory Finding Laws Link Literature Lymph Node Involvement Malignant Neoplasms Malignant neoplasm of ovary Medical Records Methodology Methods Modeling Molecular Morphology Natural Language Processing Neoplasm Metastasis Non-Insulin-Dependent Diabetes Mellitus Patients Pediatric Hospitals Pharmacogenomics Phase Phenotype Principal Investigator Process Public Health Recording of previous events Research Research Personnel Research Project Grants Rheumatoid Arthritis Sclerosis Selection for Treatments Software Design Source Structure System Testing Text Translational Research Treatment outcome Tumor Volume Uncertainty United States National Institutes of Health Universities Variant Visual Work anticancer research cancer classification cancer genome cancer genomics cancer initiation chemotherapeutic agent cost design genomic data individual patient information organization insight interest laboratory development malignant breast neoplasm melanoma new technology novel oncology open source outcome prediction precision medicine programs prototype public health relevance research and development software development trait translational cancer research translational scientist treatment response tumor usability

项目摘要

DESCRIPTION (provided by applicant): Precise phenotype information is needed to advance translational cancer research, particularly to unravel the effects of genetic, epigenetic, and othe factors on tumor behavior and responsiveness. Examples of phenotypic variables in cancer include: tumor morphology (e.g. histopathologic diagnosis), co-morbid conditions (e.g. associated immune disease), laboratory findings (e.g. gene amplification status), specific tumor behaviors (e.g. metastasis) and response to treatment (e.g. effect of a chemotherapeutic agent on tumor). Current models for correlating EMR data with -omics data largely ignore the clinical text, which remains one of the most important sources of phenotype information for cancer patients. Unlocking the value of clinical text has the potential to enable new insights about cancer initiation, progression, metastasis, and response to treatment. We propose further collaboration of two mature informatics groups with long histories of developing open-source natural language processing (NLP) software (Apache cTAKES, caTIES and ODIE) to extend existing software with new methods for cancer deep phenotyping. Several aims propose investigation of biomedical information extraction where there has been little or no previous work (e.g. clinical genomic entities, and causal discourse). Visualization of extracted data, usability of the software, and dissemination are also emphasized. Three driving oncology projects led by accomplished translational investigators in Breast Cancer, Melanoma, and Ovarian Cancer will drive development of the software. These labs will contribute phenotype variables for extraction, test utility and usability of the software, and provide the setting for a extrinsic evaluation. The proposed research bridges novel methods to automate cancer deep phenotype extraction from clinical text with emerging standards in phenotype knowledge representation and NLP. This work is highly aligned with recent calls in the scientific literature o advance scalable and robust methods of extracting and representing phenotypes for precision medicine and translational research.

描述（由申请人提供）：需要精确的表型信息来推进转化癌症研究，特别是揭示遗传、表观遗传和其他因素对肿瘤行为和反应性的影响。癌症表型变量的例子包括：肿瘤形态（例如组织病理学诊断）、共病状况（例如相关免疫疾病）、实验室检查结果（例如基因扩增状态）、特定肿瘤行为（例如转移）和对治疗的反应（例如效果）化学治疗剂对肿瘤的作用）。当前将 EMR 数据与组学数据相关联的模型在很大程度上忽略了临床文本，而临床文本仍然是癌症患者表型信息的最重要来源之一。释放临床文本的价值有可能使人们对癌症的发生、进展、转移和治疗反应产生新的见解。我们建议两个具有悠久开发开源自然语言处理（NLP）软件（Apache cTAKES、caTIES 和 ODIE）历史的成熟信息学小组进一步合作，以利用癌症深度表型分析的新方法扩展现有软件。有几个目标提出了对生物医学信息提取的研究，而以前的工作很少或根本没有（例如临床基因组实体和因果话语）。还强调了提取数据的可视化、软件的可用性和传播。由乳腺癌、黑色素瘤和卵巢癌领域卓有成效的转化研究人员领导的三个肿瘤学项目将推动该软件的开发。这些实验室将为软件的提取、测试实用性和可用性提供表型变量，并为外部评估提供设置。拟议的研究将自动从临床文本中提取癌症深层表型的新方法与表型知识表示和 NLP 的新兴标准联系起来。这项工作与科学文献中最近提出的呼吁高度一致，即为精准医学和转化研究提出先进的可扩展和稳健的提取和表示表型的方法。