基于模型分类的医院分诊偏倚检测

Ting Patrick, Sahu Aayaan, Wajge Nishad, Rao Vineet, Poosarla Hiresh, Mui Phil
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摘要

背景:鉴于2019冠状病毒病大流行及其带来的健康危机,我们的目标是开发广泛的机器学习技术,以便在医院分诊过程中清晰地了解特定患者的治疗情况和可能的虐待情况。目的:我们旨在揭示患者的治疗和医院处置是否与以下属性相关-紧急严重程度指数(ESI),性别,就业状况,保险状况,种族或民族,我们的100 MB数据集包括。材料和方法:我们的工作分为两部分-分类任务和数据分析。作为分类任务的一部分,我们使用了k-Nearest-Neighbor分类器、F1-score和随机森林。然后,我们使用SHapley加性解释(SHAP)值来分析数据,以确定每个属性的重要性。结果:我们的研究结果表明,每个属性的显著性各不相同。值得注意的是,我们发现参加私人保险计划的患者比参加联邦医疗保健计划(如医疗补助、医疗保险)的患者得到更好的治疗。此外,对于任何给定的ESI水平,患者的种族对40岁以下患者的治疗有更大的影响。令人惊讶的是,我们的发现表明语言并不是治疗过程中的障碍。讨论和结论:因此,我们得出结论,尽管医院可能不是故意这样做的,但在医院针对特定患者人口统计学进行分诊时存在系统性偏差。对于未来的工作,我们希望从医院收集更多的患者数据,以发现美国不同地区的特定人口统计数据是否能获得更好的医疗保健。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Using Model Classification to detect Bias in Hospital Triaging
Background: In light of the COVID-19 pandemic and the health crisis left in its wake, our goal is to develop extensive machine-learning techniques to provide a clear picture of the treatment, and possible mistreatment, of specific patient demographics during hospital triaging. Objective: We aim to reveal whether a patient’s treatment and hospital disposition is related to the following attributes - Emergency Severity Index (ESI), gender, employment status, insurance status, race, or ethnicity which our 100 MB dataset included. Materials and methods: Our work is separated into two parts - the classification task and data analysis. As part of the classification task, we used the k-Nearest-Neighbor classifier, the F1-score, and a random forest. We then analyze the data using SHapley Additive exPlanations (SHAP) values to determine the importance of each attribute. Results: Our findings show that significance varies for each attribute. Notably, we found that patients with private insurance programs receive better treatment compared to patients with federal-run healthcare programs (e.g. Medicaid, Medicare). Furthermore, a patient’s ethnicity has a greater impact on treatment for patients under 40 years of age for any given ESI level. Surprisingly, our findings show language is not a barrier during treatment. Discussion and conclusion: We, therefore, conclude that although hospitals may not be doing so intentionally, there is a systemic bias in hospital triaging for specific patient demographics. For future works, we hope to aggregate additional patient data from hospitals to find whether specific demographics of patients receive better healthcare in different parts of the United States.
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