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: 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
• 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

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，特别是解开遗传、表观遗传和其他因素对肿瘤行为和反应性的影响。癌症中的表型变量的实例包括：肿瘤形态学（例如，组织病理学诊断）、共病状况（例如，相关免疫疾病）、实验室发现（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，化疗剂对肿瘤的作用）。目前用于将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，其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、转移和治疗反应有新的认识。我们建议两个成熟的信息学小组进一步合作，开发开源自然语言处理（NLP）软件（Apache cTAKES，cATIES和ODIE），以扩展现有的软件与癌症深度表型的新方法。有几个目标提出了生物医学信息提取的调查，有很少或没有以前的工作（如临床基因组实体，因果关系的话语）。提取的数据，软件的可用性和传播的可视化也得到了强调。由乳腺癌、黑色素瘤和卵巢癌领域的翻译研究人员领导的三个推动肿瘤学项目将推动软件的开发。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，并为外部评价提供设置。拟议的研究将新方法与表型知识表示和NLP的新兴标准联系起来，以自动化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，即先进的可扩展和强大的方法，用于提取和代表精准医学和转化研究的表型。