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使用公平算法对现实世界数据的公平算法量化侵袭性甲素耐药黄金葡萄球菌感染的健康结果差异

Inyoung Jun¹, Sarah E Ser, Scott A Cohen

¹Department of Epidemiology, University of Florida, Gainesville, FL 32611, USA.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing

|December 31, 2023

概括

人工智能 (AI) 的公平性算法在甲素耐药黄金葡萄球菌 (MRSA) 感染中发现了健康结果的差异. 健康的社会决定因素和临床因素,包括并发症,显著影响了这些人口统计学上的差异.

科学领域:

公共卫生公共卫生
医疗信息学医疗信息学
人工智能的人工智能

背景情况:

侵袭性甲素耐药黄金葡萄球菌 (MRSA) 感染带来了重大的健康挑战.
了解健康结果差异对于公平的医疗保健服务至关重要.
健康的社会决定因素 (SDoH) 在感染结果中起着复杂的作用.

研究的目的:

用一种新的AI公平算法量化侵入性MRSA感染的健康结果差异.
确定将SDoH,临床因素和人口变量与MRSA感染结果联系起来的因果途径.
在公共卫生中展示公平性AI的实际应用.

主要方法:

利用佛罗里达州一家大型医疗保健提供商9年来的电子健康记录 (EHR) 数据.
开发了一个因果结构图,将SDoH与临床测量,治疗和结果联系起来.
应用了公平意识的因果关系 (FACTS) 分解算法来量化结果差异.

主要成果:

鉴定了与人口统计和SDoH相关的MRSA感染结果中的中等差异.
伴随性疾病是导致年龄,性别,种族和收入差异的关键因素.
具体因素如先前的功能障碍,万科米辛的使用和时间与种族差异有关.
收入,农村和医疗保健设施的可用性有助于性别差异.

结论:

公平性AI方法为分析公共卫生中的健康差异提供了实际实用性.
干预措施必须整合临床因素和SDoH,以解决MRSA感染结果差异.
解决差异需要采取多方面的方法,考虑个人临床数据和更广泛的社会背景.

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