评估人工智能在预测肺部瘤患者住院时间方面的能力,这些患者通过单门视频辅助胸腔镜手术进行管理:一项回顾性观察性研究
Issa Alnajjar1, Baraa Alshakarnah2, Tasneem AbuShaikha1
1Faculty of Medicine, Al-Quds University, Jerusalem, Palestine.
BMC surgery
|May 19, 2025
概括
人工智能 (AI) 模型在预测接受手术的肺 empyema 患者住院时间时的准确性较差. 临床变异性和当前的人工智能局限性对准确的停留时间预测构成挑战.
科学领域:
- 胸部外科手术 胸部外科手术
- 医学中的人工智能.
- 医疗信息学医学信息学
背景情况:
- 腹是严重的疾病,需要手术干预.
- 单通道视频辅助胸腔镜手术 (UVATS) 是一种对腹腔瘤的微创方法.
- 预测住院时间 (LOS) 对资源管理和患者护理至关重要.
研究的目的:
- 评估人工智能 (AI) 模型的适用性,以预测在接受UVATS治疗的肺部瘤患者中LOS的可行性.
- 将标准AI模型的预测性能与基于文献的AI模型进行比较.
主要方法:
- 对56名患有多发性 empyema 的患者进行了回顾性观察性研究.
- 开发了两种人工智能模型:一种使用患者临床数据的随机森林回归器和一种基于文献的预测模型.
- 模型性能使用平均绝对误差 (MAE) 和R平方 (R2) 值进行评估.
主要成果:
- 两种人工智能模型都表现出LOS的预测准确性不佳.
- 随机森林回归模型的MAE为4.56天,R2值为负.
- 基于文献的模型产生了类似的结果,MAE为4.53天,R2值为负.
结论:
- 目前的AI模型由于显著的临床变异性,在对膜静脉瘤患者的LOS准确预测方面存在局限性.
- 需要进一步的研究,重点关注更大的多中心数据集和先进的机器学习技术,以提高预测准确度.
- 人工智能显示出临床决策支持的潜力,但为了在这个患者群体中可靠地预测LOS,需要精细化.
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