使用个人,邮政编码衍生和机器学习模型预测的纽约市的教育成就来预测心血管疾病住院治疗的比较
Kullaya Takkavatakarn1,2, Yang Dai3, Huei Hsun Wen1
1Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America.
PloS one
|February 8, 2024
概括
机器学习 (ML) 可以预测个人的教育成就,提高健康的社会决定因素 (SDOH) 数据的准确性. 与区域级数据相比,ML预测的SDOH措施增强了心血管疾病住院预测模型.
科学领域:
- 医疗信息学 医疗信息学
- 机器学习应用 机器学习应用
- 健康研究的社会决定因素研究
背景情况:
- 健康的区域级社会决定因素 (SDOH) 常用于研究.
- 个人级别的SDOH数据,如教育程度,往往无法获得.
- 机器学习 (ML) 对于推导单个SDOH措施的实用性尚未被探索.
研究的目的:
- 调查使用ML来预测个人教育成就的可行性.
- 为了比较ML预测的SDOH与传统的区域级SDOH指标的性能.
- 评估不同SDOH数据类型对心血管疾病 (CVD) 住院预测模型的影响.
主要方法:
- 使用来自西奈山生物库的数据进行了回顾性研究.
- 开发了ML算法,从临床和人口统计数据中预测个人的教育成就.
- 使用调查衍生,邮政编码衍生和ML预测的教育成就构建了三种CVD住院预测模型.
主要成果:
- ML模型预测的教育与调查数据的一致性 (67%) 比邮政代码级教育 (47%) 高.
- 使用调查教育的模型具有最高的预测性能 (AUROC=0.77).
- 在CVD住院预测中,ML预测的教育 (AUROC=0.75) 在CVD住院预测中明显优于邮政编码衍生的教育 (AUROC=0.72).
结论:
- 邮政编码水平的教育成绩与个人调查数据的一致性很低.
- 机器学习技术可以提高个人SDOH数据的准确性.
- 利用ML预测的SDOH可以提高健康结果预测模型的性能.
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