在智力和发育残疾人中使用机器学习了解COVID-19感染情况
Michael D Broda1, Matthew Bogenschutz2, Parthenia Dinora3
1Virginia Commonwealth University School of Education, PO Box 842020, Richmond, VA, 23284, USA.
Disability and health journal
|March 28, 2024
概括
机器学习准确地预测了智力和发育障碍 (IDD) 患者的COVID-19. 关键预测因素包括年龄,支持需求,收入和BMI,这表明社会接触增加增加了这一群体的风险.
科学领域:
- 公共卫生 公共卫生
- 机器学习 机器学习
- 健康差异 在健康上的差异
背景情况:
- 智力和发育障碍 (IDD) 的人面临着不成比例的COVID-19影响.
- 在IDD人群中预测COVID-19感染存在重大挑战.
研究的目的:
- 评估一种机器学习模型,用于预测IDD患者的COVID-19诊断.
- 在IDD的家庭和社区服务 (HCBS) 用户中识别COVID-19诊断的关键预测因素.
主要方法:
- 合并了三个主要的IDD特定数据集,包括700多个变量.
- 开发了一个随机森林机器学习算法来预测COVID-19诊断.
- 分析了单个州内HCBS用户的随机样本.
主要成果:
- 机器学习模型在预测COVID-19诊断方面实现了62.5%的准确性.
- 主要预测因素包括较高的年龄,广泛的支持需求 (整体,医疗,行为),低收入社区,较高的医疗补助支出和高的BMI.
- 调查结果与一般人口趋势一致,表明社会接触增加可能会增加COVID-19风险.
结论:
- 机器学习在IDD人群中预测COVID-19的实用性.
- 人口和支持相关因素显著影响COVID-19风险.
- 了解这些预测因素可以为弱势群体提供有针对性的公共卫生干预措施.
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