Cancer Deep Phenotype Extraction from Electronic Medical Records

从电子病历中提取癌症深层表型

• 批准号: 9538366
• 负责人: GUERGANA K. SAVOVA
• 金额: $ 56.79万
• 依托单位: BOSTON CHILDREN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-05-06 至 2019-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9538366
• 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; Multiple Sclerosis; Natural Language Processing; Neoplasm Metastasis; Non-Insulin-Dependent Diabetes Mellitus; Patients; Pediatric Hospitals; Pharmacogenomics; Phase; Phenotype; Process; Public Health; Recording of previous events; Research; Research Personnel; Research Project Grants; Resolution; Rheumatoid Arthritis; Selection for Treatments; Software Design; Source; System; Testing; Text; Translational Research; Treatment outcome; 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; translational study; treatment response; tumor; usability

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性癌症研究，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。癌症中表型变量的例子包括：肿瘤形态（如组织病理学诊断）、合并症（如相关免疫疾病）、实验室结果（如基因扩增状态）、特定肿瘤行为（如转移）和对治疗的反应（如化疗药物对肿瘤的作用）。目前将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。解锁临床文本的价值有可能使关于癌症的发生、进展、转移和治疗反应的新见解成为可能。我们建议两个在开发开源自然语言处理（NLP）软件（Apache cTAKES、caTIES和ODIE）方面有着悠久历史的成熟信息学团队进一步合作，用癌症深层表型的新方法扩展现有软件。几个目标提出了对生物医学信息提取的调查，这些信息提取以前很少或没有工作（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的成功转化研究人员领导的三个驱动肿瘤学项目将推动该软件的开发。这些实验室将为提取、测试效用和软件可用性提供表型变量，并为外部评估提供设置。提出的研究将新方法与表型知识表示和NLP的新兴标准连接起来，以自动从临床文本中提取癌症深度表型。这项工作与科学文献中最近的呼吁高度一致，即为精确医学和转化研究推进可扩展和强大的提取和表示表型的方法。