Clinical word embeddings are extensively used in various Bio-NLP problems as
a state-of-the-art feature vector representation. Although they are quite
successful at the semantic representation of words, due to the dataset - which
potentially carries statistical and societal bias - on which they are trained,
they might exhibit gender stereotypes. This study analyses gender bias of
clinical embeddings on three medical categories: mental disorders, sexually
transmitted diseases, and personality traits. To this extent, we analyze two
different pre-trained embeddings namely (contextualized) clinical-BERT and
(non-contextualized) BioWordVec. We show that both embeddings are biased
towards sensitive gender groups but BioWordVec exhibits a higher bias than
clinical-BERT for all three categories. Moreover, our analyses show that
clinical embeddings carry a high degree of bias for some medical terms and
diseases which is conflicting with medical literature. Having such an
ill-founded relationship might cause harm in downstream applications that use
clinical embeddings.

研究分析了临床词嵌入在三个医疗类别中（精神疾病，性传播疾病和人格特征）的性别偏见。结果显示，临床词嵌入对一些医学术语和疾病存在高度偏见， BioWordVec 对于所有三个类别的偏见高于 clinical-BERT。所有这些可能会对使用临床词嵌入的下游应用程序造成伤害。