慢性阻塞性肺病患者的长期预后的机器学习和深度学习预测模型:系统性审查和元分析
Luke A Smith1, Lauren Oakden-Rayner1, Alix Bird1
1Australian Institute for Machine Learning, University of Adelaide, Adelaide, SA, Australia; School of Public Health, University of Adelaide, Adelaide, SA, Australia.
机器学习和深度学习模型对预测慢性阻塞性肺病 (COPD) 进展的现有得分具有有限的优势. 改进报告和验证对于COPD研究中的未来预后模型至关重要.
科学领域:
- 肺部医学 肺部医学
- 医疗信息学 医疗信息学
- 生物统计学 生物统计学
背景情况:
- 机器学习 (ML) 和深度学习 (DL) 越来越多地用于预测慢性阻塞性肺病 (COPD) 的长期疾病进展.
- 本研究系统地审查和元分析了ML和DL的COPD预后模型的性能,比较它们的有效性并确定研究缺陷.
结论:
- 目前的证据表明,ML和DL预后模型对已建立的COPD严重程度得分的优越性有限.
- 严格遵守报告准则 (例如,TRIPOD) 和强有力的验证对于减少偏见和提高未来COPD预后建模研究的可重复性至关重要.
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