Cancer Deep Phenotype Extraction from Electronic Medical Records

总结 需要精确的表型信息来推进转化癌症研究，特别是要解开癌症的遗传密码。 遗传、表观遗传和系统变化对肿瘤行为和反应性的影响。的实例 癌症的表型变量包括：肿瘤形态学（例如组织病理学诊断）、共病 疾病（如相关免疫疾病）、实验室检查结果（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，这仍然是 癌症患者表型信息的最重要来源。释放临床文本的价值 有可能使新的见解有关癌症的起始，进展，转移，和反应， 治疗我们建议进一步合作，通过新的癌症治疗方法来增强DeepPhe平台 深层表型分析有几个目标提出了生物医学信息提取的调查，其中有 很少或没有以前的工作（例如临床基因组学）。提取数据的可视化，软件的可用性， 传播也受到重视。一组由熟练的翻译领导的多样化的肿瘤学研究 乳腺癌、黑色素瘤、卵巢癌、结直肠癌和弥漫性大B细胞癌的研究者 淋巴瘤将证明该软件的实用性。这些实验室将贡献表型变量， 提取、测试软件的实用性和可用性，并提供外部评估的设置。的 提出的研究将新方法与从临床文本中自动提取癌症深度表型联系起来， 表型知识表示和NLP的新兴标准。这项工作与最近的 在科学文献中呼吁推进可扩展和强大的提取和表示方法， 用于精准医学和转化研究的表型。

Summary Precise phenotype information is needed to advance translational cancer research, particularly to unravel the effects of genetic, epigenetic, and systems changes 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 to enhance the DeepPhe platform 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). Visualization of extracted data, usability of the software, and dissemination are also emphasized. A diverse set of oncology studies led by accomplished translational investigators in Breast Cancer, Melanoma, Ovarian Cancer, Colorectal Cancer and Diffuse Large B-cell Lymphoma will demonstrate the utility of the software. These labs will contribute phenotype variables for extraction, test utility and usability of the software, and provide the setting for an 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 to advance scalable and robust methods of extracting and representing phenotypes for precision medicine and translational research.