Extraction of Symptom Burden from Clinical Narratives of Cancer Patients using Natural Language Processing

使用自然语言处理从癌症患者的临床叙述中提取症状负担

基本信息

批准号：
10591957
负责人：
Meliha Yetisgen
金额：
$ 27.45万
依托单位：
UNIVERSITY OF WASHINGTON
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-06-01 至 2024-04-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10591957
关键词：
Active Learning Address Administrative Supplement Adolescent Adult Affect Age Architecture Attention Cancer Patient Categories Cause of Death Child Child Health Childhood Clinical Clinical Research Collection Communities Computer software Data Data Set Diagnosis Disease Economics Electronic Health Record Employment Status Ensure Environment Ethnic Origin Ethnic group Event Family Funding Future Goals Gold Health Housing Hunger Institution Link Literature Living Arrangement Malignant Childhood Neoplasm Malignant Neoplasms Mediating Medical center Mental Health Methods Modeling National Cancer Institute Natural Language Processing Outcome Patient Care Patient-Focused Outcomes Patients Pediatric Oncology Pediatric cohort Performance Physical environment Population Poverty Psyche structure Publications Quality of life Race Recording of previous events Research Resources Risk Running Sampling Social Environment Substance abuse problem Symptoms Systems Development Technology Training Trauma United States Universities Washington Work base cancer care cancer type clinical practice cohort data standards deep learning design discrete data education access experimental study health care availability health data improved infancy innovation learning strategy novel open source patient population pediatric patients pediatrician point of care relating to nervous system social social factors social health determinants substance use

项目摘要

Project Summary/Abstract Although cancer in children and adolescents is rare, it is the leading cause of death by disease past infancy among children in the United States. The US Department of Health defines SDOH as “conditions in the environment that affect health, functioning, and quality of life outcomes and risks." There is an extensive literature base linking race, ethnicity, and SDOH to pediatric cancer outcomes. SDOH are commonly queried in pediatric clinical practice. Very few of the SDOH data points are noted as discrete data-fields such as race and ethnicity; most are documented as clinical narratives in Electronic Health Records (EHRs) which makes it difficult to collect SDOH in clinical and research settings to improve patient care and advance clinical research. We therefore propose to develop novel deep learning-based NLP technologies that can extract detailed SDOH information from EHRs of pediatric patients for secondary use. Our dataset will include clinical notes of pediatric patients from two institutions: Seattle Cancer Care Alliance (SCCA) and University of Washington Medical Center (UWMC). SCCA cohort will include only pediatric cancer patients. To ensure the generalizability of extraction approaches across different institutions and patient populations, UWMC cohort will include a random sample from general pediatric population. Our final corpus will include thousands of clinical notes of hundreds of pediatric patients over a period of ten years (1.1.2012-12.31.2021). We will design a frame-based event representation schema to capture the salient details of the following categories of SDOH: (1) health care access and quality, (2) living arrangements, (3) economic stability, (4) housing and hunger insecurity, (5) prior trauma/loss, (6) education access and quality, (7) patient and family substance use history, and (8) patient/family mental. We will use active learning to sample a diverse and representative set of notes for gold standard annotation. Given this gold standard, our goal is automated extraction of SDOH from clinical narratives of pediatric patients with deep learning-based NLP approaches. The proposed frame- based event representation, active learning framework and NLP architectures will be based on ongoing work from our ITCR - R21 project titled “Extraction of Symptom Burden from Clinical Narratives of Cancer Patients using Natural Language Processing” (1 R21 CA258242-01). All models and their implementations produced during the execution of this project will be shared with the community as open-source resources.

项目概要/摘要尽管儿童和青少年癌症很少见，但它是婴儿期以上疾病导致死亡的主要原因美国卫生部将 SDOH 定义为“儿童状况”。影响健康、功能和生活质量的结果和风险。” 将种族、民族和 SDOH 与儿科癌症结果联系起来的文献库经常受到质疑。儿科临床实践中很少有 SDOH 数据点被标记为离散数据字段，例如种族和种族；大多数记录为电子健康记录 (EHR) 中的临床叙述，这使得很难在临床和研究环境中收集 SDOH 以改善患者护理和推进临床研究。因此，我们建议开发新颖的基于深度学习的 NLP 技术，可以提取详细的 SDOH 我们的数据集将包括儿科患者电子病历中的临床记录，以供二次使用。来自两个机构的儿科患者：西雅图癌症护理联盟 (SCCA) 和华盛顿大学医疗中心 (UWMC)。SCCA 队列将仅包括儿童癌症患者。提取方法在不同机构和患者群体中的普遍性，UWMC 队列将包括来自一般儿科人群的随机样本，我们的最终语料库将包括数千个临床样本。我们将设计一个十年期间（2012 年 1 月 1 日至 2021 年 12 月 31 日）数百名儿科患者的记录。基于帧的事件表示模式，用于捕获以下 SDOH 类别的显着细节： (1) 医疗保健的获取和质量，(2) 生活安排，(3) 经济稳定，(4) 住房和饥饿不安全感，(5) 先前的创伤/损失，(6) 教育机会和质量，(7) 患者和家庭药物使用史， (8) 患者/家属心理我们将利用主动学习来抽取一组多样化且有代表性的笔记。对于金标准注释，我们的目标是从金标准中自动提取 SDOH。使用基于深度学习的 NLP 方法对儿科患者进行临床叙述。基于事件表示、主动学习框架和 NLP 架构将基于正在进行的工作来自我们的 ITCR - R21 项目，标题为“从癌症患者的临床叙述中提取症状负担” 使用自然语言处理”（1 R21 CA258242-01）生成的所有模型及其实现。在该项目执行期间将作为开源资源与社区共享。