儿童机器学习衍生的喘和过敏轨迹:系统性审查和元分析
Daniil Lisik1, Saliha Selin Özuygur Ermis2, Gregorio Paolo Milani3,4
1Krefting Research Centre, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden daniil.lisik@gu.se.
概括
儿童喘和湿疹遵循一致的模式,产前暴露于烟雾是关键的危险因素. 需要进一步的研究来了解过敏多病症和其他过敏轨迹.
科学领域:
- 儿科过敏和免疫学
- 计算流行病学计算流行病学
- 医疗保健中的机器学习
背景情况:
- 许多研究使用机器学习来描述儿童喘和过敏轨迹.
- 现有的研究结果由于方法的多样性而呈现出混合的结果.
- 本研究旨在综合和批判性地评估这些轨迹的证据.
研究的目的:
- 通过机器学习确定了儿童喘和过敏轨迹的现有证据.
- 批判性地评估这些研究中使用的方法.
- 确定共同的轨迹和相关的风险因素.
主要方法:
- 进行了对10个数据库的系统搜索.
- 研究选,数据提取和质量评估都是重复进行的.
- 轨迹特征被表化和可视化,风险因素和结果估计通过随机效应元分析汇总.
主要成果:
- 89项研究确定了共同的轨迹:早期发作持续性,中期发作持续性喘息/湿疹,早期发作缓解喘息/湿疹.
- 男性性别,父母的疾病和产前/产后烟雾暴露与特定的轨迹有关.
- 大多数研究 (69%) 的方法质量很低,对过敏多病症,鼻炎和食物过敏的调查有限.
结论:
- 儿童喘/喘息和湿疹表现出一致的轨迹,具有可识别的风险因素,如烟雾暴露.
- 需要改进的计算方法来更好地概括和验证中间/过渡轨迹.
- 进一步的研究应该集中在过敏性多病症和过敏性鼻炎和食物过敏的轨迹上.
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