全球共享可靠信息:基于人工智能的医疗保健策略需要外部验证. 这是一份职位论文
F Pennestrì1, F Cabitza2,3, N Picerno2
1Direzione Scientifica, IRCCS Istituto Ortopedico Galeazzi, Via Cristina Belgioioso 173, 20157, Milano, MI, Italy. federico.pennestri@grupposandonato.it.
BMC medical informatics and decision making
|February 5, 2025
概括
机器学习对COVID-19的预测根据医院数据来源而异. 在严重的COVID-19患者数据上训练的模型与在二次诊断数据上训练的模型不同,这凸显了验证的必要性.
科学领域:
- 医疗信息学 医疗信息学
- 流行病学 流行病学
- 机器学习 机器学习
背景情况:
- 机器学习 (ML) 模型越来越多地用于公共卫生.
- 数据异质性可能会影响ML模型的性能.
- 在不同的医院设置中,COVID-19患者数据在不同医院设置之间存在显著差异.
研究的目的:
- 调查数据源对ML模型对COVID-19的预测的影响.
- 评估在专科医院与普通医院的数据上训练的ML模型的可靠性.
主要方法:
- 对ML模型预测的比较分析.
- 利用来自两个地理上相邻的医院的数据,具有不同的COVID-19患者人口统计数据.
- 根据数据来源评估模型性能.
主要成果:
- 在严重的COVID-19患者数据上训练的ML模型显示,与在COVID-19是次要诊断的数据上训练的模型相比,预测有显著差异.
- 尽管医院在20公里范围内,但观察到差异.
结论:
- 训练数据的来源和性质极大地影响了ML模型对COVID-19的预测.
- 外部验证和持续监测对于确保ML驱动公共卫生工具的安全性和可靠性至关重要.
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