Suppr超能文献

比较从临床和社交媒体数据中得出的自杀风险洞察。

Comparing Suicide Risk Insights derived from Clinical and Social Media data.

机构信息

Department of Population Health Sciences, Weill Cornell Medicine, USA.

Artificial Intelligence Institute, University of South Carolina, USA.

出版信息

AMIA Jt Summits Transl Sci Proc. 2021 May 17;2021:364-373. eCollection 2021.

Abstract

Suicide is the 10 leading cause of death in the US and the 2 leading cause of death among teenagers. Clinical and psychosocial factors contribute to suicide risk (SRFs), although documentation and self-expression of such factors in EHRs and social networks vary. This study investigates the degree of variance across EHRs and social networks. We performed subjective analysis of SRFs, such as self-harm, bullying, impulsivity, family violence/discord, using >13.8 Million clinical notes on 123,703 patients with mental health conditions. We clustered clinical notes using semantic embeddings under a set of SRFs. Likewise, we clustered 2180 suicidal users on r/SuicideWatch (~30,000 posts) and performed comparative analysis. Top-3 SRFs documented in EHRs were depressive feelings (24.3%), psychological disorders (21.1%), drug abuse (18.2%). In r/SuicideWatch, gun-ownership (17.3%), self-harm (14.6%), bullying (13.2%) were Top-3 SRFs. Mentions of Family violence, racial discrimination, and other important SRFs contributing to suicide risk were missing from both platforms.

摘要

自杀是美国的第 10 大主要死因,也是青少年的第 2 大主要死因。临床和社会心理因素导致自杀风险 (SRFs),尽管电子健康记录 (EHRs) 和社交网络中对此类因素的记录和自我表达存在差异。本研究调查了 EHRs 和社交网络之间的差异程度。我们使用 123,703 名患有精神健康状况的患者的超过 1380 万份临床记录,对自杀风险因素(如自残、欺凌、冲动、家庭暴力/不和)进行了主观分析。我们使用语义嵌入在一组自杀风险因素下对临床记录进行聚类。同样,我们对 r/SuicideWatch 上的 2180 名自杀用户(约 30,000 个帖子)进行了聚类,并进行了比较分析。在 EHR 中记录的前 3 个自杀风险因素是抑郁感 (24.3%)、心理障碍 (21.1%)、药物滥用 (18.2%)。在 r/SuicideWatch 上,枪支拥有 (17.3%)、自残 (14.6%)、欺凌 (13.2%) 是前 3 个自杀风险因素。两个平台都没有提到家庭暴力、种族歧视和其他导致自杀风险的重要自杀风险因素。

相似文献

10
[Social pain at the core of suicidal behavior].[社会痛苦是自杀行为的核心]
Encephale. 2019 Jan;45 Suppl 1:S7-S12. doi: 10.1016/j.encep.2018.09.005. Epub 2018 Nov 11.

本文引用的文献

4
Evaluating the predictability of medical conditions from social media posts.从社交媒体帖子评估医疗状况的可预测性。
PLoS One. 2019 Jun 17;14(6):e0215476. doi: 10.1371/journal.pone.0215476. eCollection 2019.
9
Predicting Suicidal Behavior From Longitudinal Electronic Health Records.从纵向电子健康记录预测自杀行为。
Am J Psychiatry. 2017 Feb 1;174(2):154-162. doi: 10.1176/appi.ajp.2016.16010077. Epub 2016 Sep 9.

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验