标题： 以数据为中心的方法检测和减轻儿童心理健康文本中的人口统计学偏差：焦虑检测案例研究 显示英文标题

标题： A Data-Centric Approach to Detecting and Mitigating Demographic Bias in Pediatric Mental Health Text: A Case Study in Anxiety Detection

摘要： 介绍：医疗AI模型通常会从其训练数据中继承偏见。尽管努力主要集中在结构化数据中的偏见上，但心理健康高度依赖于非结构化数据。本研究旨在检测和减轻用于辅助儿童心理健康筛查的AI模型训练数据中与非生物学差异相关的语言差异。我们的目标是：（1）通过评估性别子组的结果公平性来评估偏见的存在，（2）通过文本分布分析识别偏见来源，（3）开发一种针对心理健康文本数据的去偏方法。方法：我们检查了不同人口统计群体之间的分类公平性，并评估了性别化语言如何影响模型预测。应用了一种以数据为中心的去偏方法，专注于中和有偏术语，同时保留显著的临床信息。该方法在针对儿童患者自动焦虑检测的模型上进行了测试。结果：我们的研究结果揭示了对女性青少年患者的系统性误诊，与男性患者相比，准确率低4%，假阴性率（FNR）高9%，这可能是由于信息密度和患者记录中的语言差异所致。男性患者的记录平均长500个单词，语言相似性指标表明性别之间的词语分布存在差异。实施我们的去偏方法将诊断偏差减少了高达27%，证明了其在提高不同人口群体之间公平性方面的有效性。讨论：我们开发了一个以数据为中心的去偏框架，以解决临床文本中的基于性别的内容差异。通过中和有偏语言并增强对临床关键信息的关注，我们的方法展示了一种有效缓解基于文本训练的医疗AI模型中偏见的策略。 显示英文摘要

摘要： Introduction: Healthcare AI models often inherit biases from their training data. While efforts have primarily targeted bias in structured data, mental health heavily depends on unstructured data. This study aims to detect and mitigate linguistic differences related to non-biological differences in the training data of AI models designed to assist in pediatric mental health screening. Our objectives are: (1) to assess the presence of bias by evaluating outcome parity across sex subgroups, (2) to identify bias sources through textual distribution analysis, and (3) to develop a de-biasing method for mental health text data. Methods: We examined classification parity across demographic groups and assessed how gendered language influences model predictions. A data-centric de-biasing method was applied, focusing on neutralizing biased terms while retaining salient clinical information. This methodology was tested on a model for automatic anxiety detection in pediatric patients. Results: Our findings revealed a systematic under-diagnosis of female adolescent patients, with a 4% lower accuracy and a 9% higher False Negative Rate (FNR) compared to male patients, likely due to disparities in information density and linguistic differences in patient notes. Notes for male patients were on average 500 words longer, and linguistic similarity metrics indicated distinct word distributions between genders. Implementing our de-biasing approach reduced diagnostic bias by up to 27%, demonstrating its effectiveness in enhancing equity across demographic groups. Discussion: We developed a data-centric de-biasing framework to address gender-based content disparities within clinical text. By neutralizing biased language and enhancing focus on clinically essential information, our approach demonstrates an effective strategy for mitigating bias in AI healthcare models trained on text.