Adverse Childhood Experiences (ACEs) are defined as a collection of highly stressful, and potentially traumatic, events or circumstances that occur throughout childhood and/or adolescence. They have been shown to be associated with increased risks of mental health diseases or other abnormal behaviours in later lives. However, the identification of ACEs from free-text Electronic Health Records (EHRs) with Natural Language Processing (NLP) is challenging because (a) there is no NLP ready ACE ontologies; (b) there are limited cases available for machine learning, necessitating the data annotation from clinical experts. We are currently developing a tool that would use NLP techniques to assist us in surfacing ACEs from clinical notes. This will enable us further research in identifying evidence of the relationship between ACEs and the subsequent developments of mental illness (e.g., addictions) in large-scale and longitudinal free-text EHRs, which has previously not been possible.
翻译:不良儿童经验被定义为在童年和/或青少年时期发生的高度紧张和潜在的创伤性事件或情况,已经证明它们与晚年生活中心理健康疾病或其他不正常行为的风险增加有关,然而,从免费文本电子健康记录(EHRs)和自然语言处理(NLP)中识别ACE具有挑战性,因为(a) 没有国家语言方案准备的ACE肿瘤;(b) 可用于机器学习的病例有限,需要临床专家提供数据说明。我们目前正在开发一种工具,利用国家语言方案技术协助我们从临床笔记中冲洗ACE,这将使我们能够进一步研究在大规模和纵向自由EHR中发现ACE与随后出现的精神疾病(例如成瘾)之间的关系的证据,而这种证据以前是不可能做到的。