论文标题
将医疗对话分为肥皂部分进行公平性
Towards Fairness in Classifying Medical Conversations into SOAP Sections
论文作者
论文摘要
随着机器学习算法在医疗保健中的部署更加广泛，算法公平性的问题变得更加至关重要。我们的工作旨在识别和理解部署模型中的差异，将医生对话分为医学肥皂说明的各个部分。我们采用多个指标来衡量分类器性能的差异，并在一部分处境不利的群体中发现微小的差异。对这些对话中的语言进行更深入的分析，并进一步分层这些群体表明，这些差异与医疗任命的类型有关，并且通常归因于医学任命类型（例如，精神病与内科医生）。我们的发现强调了了解数据本身可能存在的差异的重要性，以及如何影响模型平均分配福利的能力。
As machine learning algorithms are more widely deployed in healthcare, the question of algorithmic fairness becomes more critical to examine. Our work seeks to identify and understand disparities in a deployed model that classifies doctor-patient conversations into sections of a medical SOAP note. We employ several metrics to measure disparities in the classifier performance, and find small differences in a portion of the disadvantaged groups. A deeper analysis of the language in these conversations and further stratifying the groups suggests these differences are related to and often attributable to the type of medical appointment (e.g., psychiatric vs. internist). Our findings stress the importance of understanding the disparities that may exist in the data itself and how that affects a model's ability to equally distribute benefits.
