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患者遵守和急诊室使用的差异:一种机器学习方法

Wyatt P Bensken¹, Guadalupe Fernandez-Baca Vaca², Scott M Williams¹

¹Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, USA.

|July 2, 2023

概括

机器学习确定了在患者中预测抗发作药物坚持和急诊室 (ED) 使用的因素. 坚持因种族而异,不同的并发症影响了跨群体的坚持和ED访问.

科学领域:

神经学神经学
医疗保健服务研究医疗服务研究
数据科学数据科学数据科学

背景情况:

的治疗需要始终坚持服用抗发作药物 (ASM).
高急诊室 (ED) 使用率是病护理的重大负担.
确定坚持和ED使用的预测因素可以改善患者的治疗结果和资源分配.

研究的目的:

在患者中确定预测较低ASM坚持和高ED利用率的因素组合.
探索这些预测因子在不同种族和种族群体中的变化.

主要方法:

在2年的随访期间,利用了52,175名患者的Medicaid索赔数据.
雇员分类和回归树 (CART) 和基于3年基线数据的随机森林分析.
包括人口统计,疾病严重程度,并发症和县级社会因素;按种族和种族分层分析.

主要成果:

在CART模型中,发育障碍,年龄,种族/族裔和以前的使用被确定为坚持的顶级预测因素.
诸如发育障碍,高血压和精神疾病等并发症在跨种族/族群的坚持预测方面存在差异.
ED利用预测因素包括以前的伤害,焦虑/情绪障碍,头痛,背部问题和尿路感染;头痛是黑人个体的关键预测因素.

结论:

根据种族和种族,ASM坚持模式和相关的并发症有很大的差异.
虽然整体ED使用在种族群体之间没有差异,但特定的并发症组合不同地预测了高ED使用率.

关键词:

坚持的坚持坚持的坚持不平等的差异差异的差异应急部门利用情况.机器学习机器学习

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