用AI从死亡报告中挖掘社交孤立线索，发现高危人群特征。
Identifying social isolation themes in NVDRS text narratives using topic modeling and text-classification methods
- 结合主题建模与分类模型，从文本中识别社交孤立
- 30万自杀案例中发现1198例长期孤立，男性、同性恋者风险更高
- 可为公共卫生干预提供数据支持，适合政策研究者参考
近年来，社交孤立和孤独感持续上升，显著影响自杀率。尽管美国全国暴力死亡报告系统（NVDRS）的结构化变量未记录此类信息，但可通过自然语言处理技术从执法和法医医学记录中提取相关表述。本研究采用主题建模生成词典，并结合监督学习分类器，构建出高性能分类模型（平均F1：0.86，准确率：0.82）。在2002至2020年超过30万起自杀案例中，识别出1,198例提及长期社交孤立。结果显示，男性（OR = 1.44；95% CI: 1.24, 1.69；p<.0001）、同性恋者（OR = 3.68；95% CI: 1.97, 6.33；p<.0001）及离异者（OR = 3.34；95% CI: 2.68, 4.19；p<.0001）更可能被归类为长期孤立。此外，近期或即将离婚、子女抚养权丧失、被迫搬迁或搬家、关系破裂等也均为显著预测因素。该方法有助于提升美国对社交孤立与孤独的监测与预防能力。
原文摘要 · Abstract (English)
Social isolation and loneliness, which have been increasing in recent years strongly contribute toward suicide rates. Although social isolation and loneliness are not currently recorded within the US National Violent Death Reporting System's (NVDRS) structured variables, natural language processing (NLP) techniques can be used to identify these constructs in law enforcement and coroner medical examiner narratives. Using topic modeling to generate lexicon development and supervised learning classifiers, we developed high-quality classifiers (average F1: .86, accuracy: .82). Evaluating over 300,000 suicides from 2002 to 2020, we identified 1,198 mentioning chronic social isolation. Decedents had higher odds of chronic social isolation classification if they were men (OR = 1.44; CI: 1.24, 1.69, p<.0001), gay (OR = 3.68; 1.97, 6.33, p<.0001), or were divorced (OR = 3.34; 2.68, 4.19, p<.0001). We found significant predictors for other social isolation topics of recent or impending divorce, child custody loss, eviction or recent move, and break-up. Our methods can improve surveillance and prevention of social isolation and loneliness in the United States.
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