人工智能在静脉血栓栓塞的预测:一个系统的审查和聚合分析
Thita Chiasakul1,2,3, Barbara D Lam1,2, Megan McNichol4
1Division of Hematology, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA.
European journal of haematology
|October 4, 2023
概括
人工智能 (AI) 在提高静脉血栓塞栓症 (VTE) 预测准确度方面表现有前途,与传统方法相比. 然而,研究中偏差的高风险限制了目前的临床适用性.
科学领域:
- 医疗信息学 医疗信息学
- 生物统计学 生物统计学
- 医疗保健中的机器学习
背景情况:
- 精确预测静脉血栓塞栓症 (VTE) 对于有效的患者管理至关重要.
- 人工智能 (AI) 提供了在大型数据集中识别复杂预测模式的潜力.
- 目前缺乏对人工智能在VTE预测中的表现的全面分析.
研究的目的:
- 系统地审查和分析AI在VTE预测中的诊断和预后性能.
- 将人工智能模型的有效性与已建立的临床风险评估模型 (RAM) 和后勤回归进行比较.
主要方法:
- 在主要数据库 (PubMed,MEDLINE,EMBASE,Web of Science) 进行了系统的文献搜索,截至2021年4月20日.
- 包括评估成人VTE预测AI的研究,报告关键性能指标 (灵敏度,特异性,AUC).
- 使用PROBAST评估偏差风险,并使用未配对的t-test比较AI和传统方法之间的平均AUC.
主要成果:
- 包括20项涉及31至111,888名参与者的研究,使用各种AI模型.
- 人工智能模型显示,与传统方法 (0.61,p < .001) 相比,AUC (0.79) 的平均值显著更高.
- 尽管表现强,但大多数研究都表现出高偏差风险,质疑临床实践中的可复制性.
结论:
- 人工智能显示出提高VTE诊断和预后准确性的潜力,而不是传统的风险模型.
- 在所有审查的研究中发现了显著的偏差风险.
- 未来的研究应该优先考虑透明的报告,外部验证和AI模型用于VTE预测的临床实施.
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