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识别现有证据,潜在地开发一个机器学习诊断算法,在初级保健设置咳:范围审查
Julia Cummerow1, Christin Wienecke1, Nicola Engler1
1Institute of Family Medicine, University Medical Centre Schleswig-Holstein, Campus Lübeck, Lübeck, Germany.
Journal of medical Internet research
|December 14, 2023
概括
对咳作为一般诊断诊断的预测指标的数据有限. 这次范围审查发现,没有足够的证据来构建可靠的机器学习诊断算法,用于与咳相关的疾病.
科学领域:
- 主要护理药物 医疗保健药物
- 临床决策的过程
- 医疗保健中的人工智能
背景情况:
- 初级保健机构的诊断复杂,原因是许多潜在的条件.
- 临床决策涉及分析推理,直觉和管理不确定性.
- 人工智能 (AI) 旨在支持诊断,需要有关诊断概率 (odds比率) 的数据.
研究的目的:
- 进行范围审查,确定咳数据作为一般实践中的诊断预测因素.
- 评估使用这些数据用于基于机器的诊断算法的可行性.
- 在一般实践中讨论这种算法的适用性.
主要方法:
- 搜索了PubMed,Scopus,科学网和科克兰图书馆的数据库.
- 包括来自德国家庭医学杂志的灰色文献.
- 专注于分析咳作为诊断预测因素的文章,不包括非原始研究和非英语/德语文章.
主要成果:
- 35篇文章符合来自1458个记录的纳入标准.
- 慢性阻塞性肺病 (COPD) 是最常见的诊断 (31%).
- 咳预测了COPD,流感,COVID-19和支气管癌,但对喘的预测是不一致的.
结论:
- 在初级保健中,咳作为预测因素的可靠数据很少.
- 现有的数据不足以开发一个有意义的机器学习诊断算法.
- 一般实践的具体情况限制了当前AI诊断模型的直接适用性.
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