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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：
10472741
负责人：
HARRY S HOCHHEISER
金额：
$ 83.88万
依托单位：
BOSTON CHILDREN'S HOSPITAL
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：
2020-09-24 至 2025-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10472741
关键词：
Academic Medical Centers Address Adjuvant Advanced Malignant Neoplasm Adverse event Area Bioinformatics Brain Aneurysms Cancer Patient Cancer Research Project Caring Case Study Characteristics Clinic Clinical Clinical Informatics Collaborations Colorectal Cancer Communities Community Clinical Oncology Program Community of Practice Complement Computer software Computerized Medical Record Computing Methodologies Consumption Dana-Farber Cancer Institute Data Data Science Decision Making Diagnosis Diagnostic Disease Epigenetic Process Evaluation Funding Gene Amplification Genetic Genomics Growth Hematologic Neoplasms Hepatotoxicity Immune System Diseases Informatics Information Retrieval Investigation Laboratory Finding Literature Malignant Neoplasms Malignant neoplasm of ovary Manuals Measures Medical Medical Records Methods Methotrexate Modeling Morphology Multiple Sclerosis Natural Language Processing Neoadjuvant Therapy Neoplasm Metastasis Oncology Pathology Patient-Focused Outcomes Patients Phenotype Postoperative Period Process Public Health Rare Diseases Recording of previous events Records Research Research Personnel Rheumatoid Arthritis Severities Solid Source Stress System Techniques Technology Testing Text Time TimeLine Translational Research Treatment Protocols Visual Visualization Work analytical tool anticancer research autism spectrum disorder base cancer care cancer initiation cancer subtypes cancer type chemotherapeutic agent chemotherapy clinical care clinical investigation cohort community building comorbidity data streams design exhaustion follow-up individual patient informatics tool information organization innovation insight interactive tool interest large cell Diffuse non-Hodgkin&apos s lymphoma malignant breast neoplasm melanoma next generation sequencing novel open source precision medicine programs response tool translational cancer research translational scientist treatment response tumor tumor behavior unstructured data usability

项目摘要

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.

总结