人工智能方法预测头癌患者在医院的术后停留时间:系统性审查
Willian Nogueira Silva1, Anna Luíza Damaceno Araújo1,2, Alvaro Sanabria3
1Head and Neck Surgery Department and LIM 28, University of São Paulo Medical School, São Paulo 05403-900, Brazil.
Diagnostics (Basel, Switzerland)
|January 28, 2026
概括
人工智能 (AI) 模型在头癌手术后预测住院时间 (LOS) 方面表现有前途. 然而,高偏差风险和方法问题限制了当前的证据,需要改善临床使用.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
- 在瘤学瘤学.
背景情况:
- 住院时间 (LOS) 是医疗管理中的关键指标.
- 预测LOS有助于资源分配和患者护理规划.
- 头 (H&N) 癌症手术患者有复杂的恢复轨迹.
研究的目的:
- 系统地审查人工智能模型在预测H&N癌症手术患者医院LOS方面的表现.
- 评估AI驱动的LOS预测在这个特定的手术队列中的准确性和局限性.
主要方法:
- 按照PRISMA 2020指南进行系统审查.
- 在多个数据库 (Cochrane,Embase,PubMed,Scopus) 和灰色文学中进行了搜索.
- 使用PROBAST评估偏差风险并进行叙事合成.
主要成果:
- 五项涉及5009名患者的研究符合纳入标准,所有这些研究都使用监督学习来预测LOS.
- 通常研究的变量包括年龄,种族,ASA得分,BMI和并发症,但它们与LOS的关联有所不同.
- 人工智能模型的准确度在0.63-0.84之间,AUROC值在0.66-0.80之间,表明中度的区分能力.
- 所有纳入的研究都显示出高偏差风险.
结论:
- 人工智能模型展示了在H&N癌症手术中预测LOS的潜力.
- 显著的局限性,包括高偏差风险和方法上的弱点,目前阻碍了可靠的应用.
- 未来的研究需要方法改进,外部验证和透明的报告,以提高可信度和临床整合.
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