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探索儿童喘管理中的机器学习应用:范围审查
Tanvi Ojha1,2, Atushi Patel1, Krishihan Sivapragasam1
1Upstream Lab, MAP Centre for Urban Health Solutions, Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, ON, Canada.
JMIR AI
|August 27, 2024
概括
机器学习 (ML) 模型在预测儿科喘结果方面表现有希望,后勤回归和随机森林是常见的. 未来的研究应该改善数据质量和模型解释性,以便在儿童喘管理中更好地临床应用.
科学领域:
- 儿科肺病学 儿科肺病学
- 计算健康 计算健康
- 生物医学信息学 生物医学信息学
背景情况:
- 机器学习 (ML) 为预测儿童喘相关结果提供了一种新的方法.
- 这种融合对于推进儿科医疗保健和喘管理策略至关重要.
研究的目的:
- 对2019年以来发表的关于儿童喘ML算法的研究进行范围审查.
- 分析这些ML模型的应用和预测性能.
- 确定ML驱动的儿科喘研究中的趋势,常用算法和关键结果.
主要方法:
- 从2019年1月到2023年7月,在主要数据库 (Ovid MEDLINE,Embase,Cochrane Library,CINAHL,Web of Science) 中进行了全面的文献搜索.
- 包括使用ML模型预测儿童 (<18岁) 喘结果的研究.
- 偏差风险使用预测模型偏差风险评估工具进行评估.
主要成果:
- 15项研究符合纳入标准,使用ML技术,如后勤回归 (47%) 和随机森林 (40%).
- 主要应用包括预测恶化的情况 (XGBoost,AUROC 0.76),分类表型 (SVM,AUROC 0.79),诊断 (ANN,AUROC 0.63),以及识别风险因素 (随机森林,AUROC 0.88).
- 大多数研究具有低至中等偏差风险,但限制包括数据质量,样本大小和可解释性.
结论:
- ML在预测儿科喘结果方面展示了多种应用,模型的强度各不相同.
- XGBoost,SVM,ANN和随机森林显示出不同喘相关结果的显著预测能力.
- 未来的研究必须优先考虑数据质量,更大的样本大小,以及用于临床翻译的增强模型解释性.
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