论文标题
确定患有严重精神疾病的人群中的身体健康合并症：SEMEHR的应用
Identifying physical health comorbidities in a cohort of individuals with severe mental illness: An application of SemEHR
论文作者
论文摘要
精神卫生服务中的多种病态研究需要来自物理健康状况的数据，这些数据传统上受到精神卫生保健电子健康记录的限制。在这项研究中，我们旨在使用SEMEHR从临床注释中提取物理健康状况的数据。数据是从伦敦南部和莫德斯利生物医学研究中心（SLAM BRC）的临床记录互动搜索（CRIS）系统中提取的，该同类群体由所有在2007年至2018年之间接受了严重精神疾病的主要或继发性诊断的个人。三对注释者注释了2403个文档，带有平均COHEN COHEN KAPPA的KAPPA的平均0.7575757.757。结果表明，NLP性能在不同疾病区域（F1 0.601-0.954）方面有所不同，这表明不同条件组的语言模式或术语对同一NLP任务带来了不同的技术挑战。
Multimorbidity research in mental health services requires data from physical health conditions which is traditionally limited in mental health care electronic health records. In this study, we aimed to extract data from physical health conditions from clinical notes using SemEHR. Data was extracted from Clinical Record Interactive Search (CRIS) system at South London and Maudsley Biomedical Research Centre (SLaM BRC) and the cohort consisted of all individuals who had received a primary or secondary diagnosis of severe mental illness between 2007 and 2018. Three pairs of annotators annotated 2403 documents with an average Cohen's Kappa of 0.757. Results show that the NLP performance varies across different diseases areas (F1 0.601 - 0.954) suggesting that the language patterns or terminologies of different condition groups entail different technical challenges to the same NLP task.
