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人工智能模型用于预测儿童喘预后
Elham Sagheb1, Chung-Il Wi2, Katherine S King3
1Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, Minn.
人工智能模型使用电子健康记录预测儿童喘缓解. 这些人工智能工具可以帮助为患有喘的儿童制定更好的护理计划.
科学领域:
- 儿科肺病学 儿科肺病学
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
背景情况:
- 儿童喘往往持续到成年期,影响长期健康.
- 预测喘缓解对于定制有效的护理策略至关重要.
- 电子健康记录 (EHR) 为预后建模提供了有价值的数据.
研究的目的:
- 开发和评估人工智能 (AI) 模型,用于预测儿童喘预后 (缓解与无缓解).
- 利用来自EHR的各种临床变量,在不同儿科年龄组进行预测建模.
- 评估各种人工智能算法在预测喘结果方面的性能.
主要方法:
- 人工智能模型使用6-15岁患者的电子病历数据进行训练.
- 使用了两个队列:一个手动注释的队列 (n=900) 和一个更大的,自动标记的队列 (n=29,594).
- 包括后勤回归,随机森林和XGBoost在内的算法用结构化和非结构化EHR数据进行了测试.
主要成果:
- 人工智能模型实现了高预测性能 (AUC 0.85-0.93),最好的模型是在12岁时实现的.
- 使用弱标记数据的模型显示了增强的预测性能.
- 使用前10个变量的模型的表现与使用所有变量的模型相比较.
结论:
- 人工智能模型使用EHR数据和有限的变量集准确预测儿童喘预后.
- 这种方法可以显著改善优先护理计划的制定和患者教育.
- 通过人工智能预测增强疾病管理可以改善患有喘的儿童的生活质量.
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