In psychiatry, negative patient descriptions and stigmatizing language can
contribute to healthcare disparities in two ways: (1) read by patients they can
harm their trust and engagement with the medical center; (2) read by future
providers they may negatively influence the future perspective of a patient. By
leveraging large language models, this work aims to identify the sentiment
expressed in psychiatric clinical notes based on the reader's point of view.
Extracting sentences from the Mount Sinai Health System's large and diverse
clinical notes, we used prompts and in-context learning to adapt three large
language models (GPT-3.5, Llama 2, Mistral) to classify the sentiment conveyed
by the sentences according to the provider or non-provider point of view.
Results showed that GPT-3.5 aligns best to provider point of view, whereas
Mistral aligns best to non-provider point of view.

精神病学中，消极的病人描述和带有污名化的语言对医疗不公平产生了负面影响，该研究利用大规模语言模型来识别基于读者视角的精神病临床记录中的情感表达。结果显示，GPT-3.5 对提供者视角最为契合，而 Mistral 对非提供者视角最为契合。

一种观点的角度：朝向精神病学笔记中临床医生偏见的检测

The Point of View of a Sentiment: Towards Clinician Bias Detection in  Psychiatric Notes

Large language models like GPT-3.5-turbo and GPT-4 hold promise for
healthcare professionals, but they may inadvertently inherit biases during
their training, potentially affecting their utility in medical applications.
Despite few attempts in the past, the precise impact and extent of these biases
remain uncertain. Through both qualitative and quantitative analyses, we find
that these models tend to project higher costs and longer hospitalizations for
White populations and exhibit optimistic views in challenging medical scenarios
with much higher survival rates. These biases, which mirror real-world
healthcare disparities, are evident in the generation of patient backgrounds,
the association of specific diseases with certain races, and disparities in
treatment recommendations, etc. Our findings underscore the critical need for
future research to address and mitigate biases in language models, especially
in critical healthcare applications, to ensure fair and accurate outcomes for
all patients.

大型语言模型在医疗专业人员中具有潜力，但在训练过程中可能意外地继承偏见，可能会影响其在医学应用中的实用性。通过定性和定量分析，我们发现这些模型倾向于为白人群体投射更高的费用和较长的住院时间，并在具有更高生存率的具有挑战性的医疗场景中表现出乐观的态度。这些偏差在生成患者背景信息、将特定疾病与某种种族关联以及治疗建议的偏差等方面都有体现。我们的发现强调了未来研究在解决和减轻语言模型的偏见问题方面的关键需求，特别是关注关键的医疗应用，以确保公平和准确的结果对所有患者来说。

揭示和量化大型语言模型在医学报告生成中的种族偏见

Unmasking and Quantifying Racial Bias of Large Language Models in  Medical Report Generation

The use of machine learning systems to support decision making in healthcare
raises questions as to what extent these systems may introduce or exacerbate
disparities in care for historically underrepresented and mistreated groups,
due to biases implicitly embedded in observational data in electronic health
records. To address this problem in the context of clinical risk prediction
models, we develop an augmented counterfactual fairness criteria to extend the
group fairness criteria of equalized odds to an individual level. We do so by
requiring that the same prediction be made for a patient, and a counterfactual
patient resulting from changing a sensitive attribute, if the factual and
counterfactual outcomes do not differ. We investigate the extent to which the
augmented counterfactual fairness criteria may be applied to develop fair
models for prolonged inpatient length of stay and mortality with observational
electronic health records data. As the fairness criteria is ill-defined without
knowledge of the data generating process, we use a variational autoencoder to
perform counterfactual inference in the context of an assumed causal graph.
While our technique provides a means to trade off maintenance of fairness with
reduction in predictive performance in the context of a learned generative
model, further work is needed to assess the generality of this approach.

本文使用机器学习系统支持决策制定在医疗保健中的问答涉及使用电子医疗记录中的观察性数据中隐含的偏见，进而进一步发展了组公平性标准，以在个体层面上扩展增益计数事实公正标准，探讨扩增的标准是否可以应用于使用观测性电子健康记录数据的病人长住和死亡的公平模型的开发。通过一个假设因果图的变分自动编码器来执行反事实推理，提供了一种在学习生成模型的背景下，在公平维护与预测性能下降之间权衡的方法。