预测儿童严重喘恶化:一个基于行政索赔的预测模型
Mandana Rezaeiahari1, Clare C Brown1, Arina Eyimina1
1College of Public Health, University of Arkansas for Medical Sciences, Little Rock, AR, USA.
概括
这项研究开发了儿童喘恶化预测模型. 虽然该模型显示了中等准确性,但种族,种族和健康的社会决定因素等因素并没有改善其性能.
科学领域:
- 儿科肺病学 儿科肺病学
- 医疗信息学 医疗信息学
- 机器学习在医疗保健中的应用
背景情况:
- 儿童喘恶化对健康造成重大负担.
- 现有的预测模型往往忽视了关键的健康社会决定因素 (SDOH) 和人口因素.
- 需要包含种族,种族和SDOH的模型来预测喘的结果.
研究的目的:
- 开发和评估儿童喘恶化预测模型.
- 探索种族,种族,农村-城市通勤区 (RUCA) 代码和儿童机会指数 (COI) 对喘结果的预测价值.
- 评估各种SDOH在预测喘相关住院和急诊室 (ED) 访问方面的重要性.
主要方法:
- 利用了来自阿肯色州所有付款人索赔数据库的保险索赔数据.
- 确定了一组22,631名5至18岁的儿童喘和持续的医疗补助入学.
- 采用条件随机森林模型来预测与喘相关的住院和急救诊所的访问.
主要成果:
- 预测模型实现了曲线下的面积 (AUC) 约为72-73%.
- 以往与喘相关的住院或急救访问是未来事件的最强有力的预测因素.
- 药物使用 (缓解剂和控制剂) 也有助于预测准确度.
结论:
- 开发的模型在预测儿童喘恶化时表现出中度的准确性.
- 种族,种族,RUCA代码,COI和ICD-10 SDOH措施并没有显著提高模型的预测能力.
- 未来的研究可能需要探索替代或改进的SDOH指标以改善预测.
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