具有成本敏感性的顺序分类方法可以预测SARS-CoV-2肺炎的严重程度.
IEEE journal of biomedical and health informatics
|February 8, 2024
概括
具有成本敏感性的常规人工智能机器学习策略可以有效预测SARS-CoV-2肺炎的严重程度. 这些先进的AI-ML模型的表现优于传统的得分,提供了更好的临床预后.
科学领域:
- 人工智能和机器学习
- 医疗信息学 医疗信息学
- 公共卫生 公共卫生
背景情况:
- 严重急性呼吸系统综合征冠状病毒2 (SARS-CoV-2) 肺炎的预后对于患者管理至关重要.
- 传统的临床严重性评分在准确预测患者结果方面存在局限性.
- 人工智能机器学习 (AI-ML) 提供了增强预测能力的潜力.
研究的目的:
- 评估成本敏感的顺序AI-ML策略对SARS-CoV-2肺炎严重程度预后的适用性.
- 开发和比较新的AI-ML模型与已建立的临床分数.
主要方法:
- 一项观察性,回顾性队列研究,涉及西班牙四家医院的1548名患者.
- 开发260个不同的AI-ML模型,使用顺序分解和成本敏感的重新采样技术.
- 通过嵌套交叉验证进行性能评估,将最佳模型与五个临床得分和标准AI-ML基线进行比较.
主要成果:
- 最好的AI-ML模型,使用没有归算,完整的功能集,顺序分区,基于成本的再平衡和梯度提升,实现了68.1%的中位精度和0.802.80的AUC.
- 该模型与所有五个临床严重性得分和标准AI-ML基线相比,表现优越.
- 该模型成功地利用了顺序信息,并管理了阶级不平衡和不对称的成本.
结论:
- 具有成本敏感性的顺序AI-ML策略适合并有效用于SARS-CoV-2肺炎严重性预后.
- 开发的AI-ML模型超过了现有的方法,突出了这些先进技术的潜力.
- 临床预后AI-ML中的普通和成本敏感方面未得到充分探索,但提供了显著的优势.
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