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，导致在生物医学研究界对研究这些疾病决定因素的兴趣更大。虽然存在一些知识的贡献，如社会经济地位，教育背景，烟草和酒精的使用，和遗传易感性的特定疾病或条件，需要增强的方法来分析和确定多个决定因素之间的相互关系，并发现潜在的意想不到的关系，可能最终有助于改善病人的护理和人口健康。电子健康记录（EHR）系统的广泛采用有可能加强收集和访问有关个人一生健康状况和医疗保健的广泛信息，以支持一系列“二次用途”，如生物医学，行为和社会科学，以及公共卫生研究。传统上，临床医生在临床笔记中记录个人的健康史，包括“社会史”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族史的特定模块， 大量的这种信息主要以叙述的形式记录，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，知识获取和发现方法可以应用于确认相对于特定疾病或病症的已知关系以及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，用于生成有关健康的各种决定因素的知识，包括其时间进展，严重程度以及与健康状况的关系。为实现这一目标，具体目标是：（1）开发综合信息模型和自然语言处理（NLP）技术，以从EHR中的社会和家族史信息中表示、提取和整合社会、行为和家族因素，（2）适应和扩展数据挖掘技术，以识别这些因素和疾病之间的非时间和时间关系，以及（3）评估和验证特定病症（小儿哮喘和癫痫）的已知和候选新关系。这个多站点提案将涉及来自佛蒙特大学和明尼苏达大学的跨学科研究人员团队，使用来自这两个机构的EHR数据，以及合作开发和评估NLP和数据挖掘技术。最终，这项工作有可能提供一个可推广的方法，支持和加强现有的知识，社会，行为和家庭因素和疾病之间的相互作用。

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.