Leveraging the EHR to Collect and Analyze Social, Behavioral & Familial Factors

描述(由申请人提供)：了解社会、行为、环境和遗传因素之间的相互作用及其与健康的关系的重要性，使生物医学研究界对研究这些疾病决定因素产生了更大的兴趣。虽然存在一些关于特定决定因素的贡献的知识，如社会经济地位、教育背景、烟酒使用以及对特定疾病或条件的遗传易感性，但需要改进方法来分析和确定多个决定因素之间的相互关系，并发现潜在的意外关系，这些关系最终可能有助于改善患者护理和人口健康。电子健康记录(EHR)系统越来越多地被采用，有可能加强收集和获取关于个人终身健康状况和健康护理的广泛信息，以支持生物医学、行为和社会科学以及公共卫生研究等一系列“二次用途”。传统上，临床医生在临床记录中记录个人的健康史，包括“社会历史”部分中的社会和行为因素，以及“家族史”部分中的家族因素。虽然一些EHR系统具有用于以结构化或半结构化格式收集社会和家庭历史的特定模块， 大量这样的信息主要是以叙事格式记录的，因此需要自动化的方法来促进社会、行为和家庭因素的提取和整合，以供后续使用。一旦提取出来，知识获取和发现方法既可以用于确认与特定疾病或疾病相关的已知关系，也可以用于潜在地发现新的关系。我们假设先进的计算方法可以将EHR中的社会、行为和家庭因素转化为丰富的纵向资源，以生成关于各种健康决定因素的知识，包括它们的时间进程、严重程度和与健康状况的关系。为了实现这一目标，具体目标是：(1)开发全面的信息模型和自然语言处理(NLP)技术，以表示、提取和整合电子病历中的社会、行为和家庭因素，(2)调整和扩展数据挖掘技术，以确定这些因素和疾病之间的非时间和时间关系，以及(3)评估和验证特定情况(儿童哮喘和癫痫)的已知和候选新关系。这项多地点提案将涉及来自佛蒙特大学和明尼苏达大学的跨学科调查小组，使用这两个机构的电子病历数据，以及合作开发和评估自然语言处理和数据挖掘技术。最终，这项工作有可能为支持和加强关于社会、行为和家庭因素与疾病之间相互作用的现有知识提供一种普遍的方法。

DESCRIPTION (provided by applicant): The importance of understanding interactions among social, behavioral, environmental, and genetic factors and their relationship to health has led to greater interest in studying these determinants of disease in the biomedical research community. While some knowledge exists regarding contributions of specific determinants such as socioeconomic status, educational background, tobacco and alcohol use, and genetic susceptibility to particular diseases or conditions, enhanced methods are needed to analyze and ascertain interrelationships among multiple determinants and to discover potentially unexpected relationships that may ultimately contribute to improving patient care and population health. The increased adoption of electronic health record (EHR) systems has the potential for enhanced collection and access to a wide range of information about an individual's lifetime health status and health care to support a range of "secondary uses" such as biomedical, behavioral and social science, and public health research. Traditionally, clinicians document an individual's health history in clinical notes, including social and behavioral factors within the "social histor" section and familial factors in the "family history" section. While some EHR systems have specific modules for collecting social and family history in structured or semi-structured formats, a large amount of this information is recorded primarily in narrative format, thus necessitating the need for automated methods to facilitate the extraction and integration of social, behavioral, and familial factors for subsequent uses. Once extracted, knowledge acquisition and discovery methods can be applied to both confirm known relationships relative to specific diseases or conditions as well as to potentially discover new relationships. We hypothesize that advanced computational methods can transform social, behavioral, and familial factors from the EHR into a rich longitudinal resource for generating knowledge regarding various determinants of health including their temporal progression, severity, and relationship to health conditions. Towards this goal, the specific aims are to: (1) develop comprehensive information models and natural language processing (NLP) techniques to represent, extract, and integrate social, behavioral, and familial factors from social and family history information in the EHR, (2) adapt and extend data mining techniques to identify non-temporal and temporal relationships among these factors and diseases, and (3) evaluate and validate known and candidate new relationships for specific conditions (pediatric asthma and epilepsy). This multi-site proposal will involve a transdisciplinary team of investigators from the University of Vermont and University of Minnesota, use of EHR data from both institutions, and collaborative development and evaluation of the NLP and data mining techniques. Ultimately, this work has the potential to provide a generalizable approach for supporting and enhancing existing knowledge regarding the interactions among social, behavioral, and familial factors and diseases.