用语气分析发现急诊精神科诊断偏见，黑人男性高负面语句者更易被误诊为精神分裂症。
Bias Detection in Emergency Psychiatry: Linking Negative Language to Diagnostic Disparities
- 用大模型识别病历中负面语句比例衡量医生偏见
- 负面语句越多，精神分裂症诊断风险越高，种族差异被削弱
- 揭示情绪化语言如何放大诊断不公，适合医疗公平研究者参考
急诊科是高压力环境，医护人员易受偏见影响。在美国，黑人患者比其他族裔更可能在急诊科首次获得精神分裂症（SCZ）诊断，这是一种高度污名化的疾病。本研究基于一所大型医疗机构的29,005名患者数据（涵盖焦虑、双相障碍、抑郁、创伤及SCZ），分析医生偏见暴露与精神科诊断之间的关联。通过大语言模型Mistral标注病历中负面语句，计算负面句子比率（NSR）作为偏见暴露指标。采用逻辑回归模型，在控制患者人口学特征和风险因素后，发现高NSR显著提高获得SCZ诊断的概率，并减弱了种族对诊断的影响。黑人男性在高NSR情况下，其被诊断为SCZ的几率最高。结果表明，基于情感的量化指标可有效操作化真实世界中的医生偏见，并揭示超越种族的诊断不平等现象。
原文摘要 · Abstract (English)
The emergency department (ED) is a high stress environment with increased risk of clinician bias exposure. In the United States, Black patients are more likely than other racial/ethnic groups to obtain their first schizophrenia (SCZ) diagnosis in the ED, a highly stigmatizing disorder. Therefore, understanding the link between clinician bias exposure and psychiatric outcomes is critical for promoting nondiscriminatory decision-making in the ED. This study examines the association between clinician bias exposure and psychiatric diagnosis using a sample of patients with anxiety, bipolar, depression, trauma, and SCZ diagnoses (N=29,005) from a diverse, large medical center. Clinician bias exposure was quantified as the ratio of negative to total number of sentences in psychiatric notes, labeled using a large language model (Mistral). We utilized logistic regression to predict SCZ diagnosis when controlling for patient demographics, risk factors, and negative sentence ratio (NSR). A high NSR significantly increased one's odds of obtaining a SCZ diagnosis and attenuated the effects of patient race. Black male patients with high NSR had the highest odds of being diagnosed with SCZ. Our findings suggest sentiment-based metrics can operationalize clinician bias exposure with real world data and reveal disparities beyond race or ethnicity.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。