@文章{信息:doi/10.2196/22397，作者="Bittar, Andr{\'e} and Velupillai, Sumithra and Roberts, Angus and Dutta, Rina"，标题="使用通用情感词汇在电子健康记录中进行自杀风险评估:基于语库的分析"，期刊="JMIR Med Inform"，年="2021"，月="Apr"，日="13"，卷="9"，数="4"，页数="e22397"，关键词="精神病学;自杀;自杀未遂;风险评估;电子健康记录;情绪分析;自然语言处理;背景:自杀是一个严重的公共卫生问题，占全球所有死亡人数的1.4 %。据报道，目前的风险评估工具在预测自杀方面的表现并不比概率好多少。研究电子健康档案动态特征的新方法正在不断地被探索。 One avenue of research involves using sentiment analysis to examine clinicians' subjective judgments when reporting on patients. Several recent studies have used general-purpose sentiment analysis tools to automatically identify negative and positive words within EHRs to test correlations between sentiment extracted from the texts and specific medical outcomes (eg, risk of suicide or in-hospital mortality). However, little attention has been paid to analyzing the specific words identified by general-purpose sentiment lexicons when applied to EHR corpora. Objective: This study aims to quantitatively and qualitatively evaluate the coverage of six general-purpose sentiment lexicons against a corpus of EHR texts to ascertain the extent to which such lexical resources are fit for use in suicide risk assessment. Methods: The data for this study were a corpus of 198,451 EHR texts made up of two subcorpora drawn from a 1:4 case-control study comparing clinical notes written over the period leading up to a suicide attempt (cases, n=2913) with those not preceding such an attempt (controls, n=14,727). We calculated word frequency distributions within each subcorpus to identify representative keywords for both the case and control subcorpora. We quantified the relative coverage of the 6 lexicons with respect to this list of representative keywords in terms of weighted precision, recall, and F score. Results: The six lexicons achieved reasonable precision (0.53-0.68) but very low recall (0.04-0.36). Many of the most representative keywords in the suicide-related (case) subcorpus were not identified by any of the lexicons. The sentiment-bearing status of these keywords for this use case is thus doubtful. Conclusions: Our findings indicate that these 6 sentiment lexicons are not optimal for use in suicide risk assessment. We propose a set of guidelines for the creation of more suitable lexical resources for distinguishing suicide-related from non--suicide-related EHR texts. ", issn="2291-9694", doi="10.2196/22397", url="https://medinform.www.mybigtv.com/2021/4/e22397", url="https://doi.org/10.2196/22397", url="http://www.ncbi.nlm.nih.gov/pubmed/33847595" }