人工智能在静脉血栓栓塞的风险评估和管理中的应用:范围审查
Yujia Gu1, Yang Yang2, Xiaojie Gao3
1College of Nursing, Zhejiang Chinese Medical University, Hangzhou, China.
Frontiers in physiology
|October 24, 2025
概括
人工智能 (AI) 与传统方法相比,显著提高了静脉血栓塞栓症 (VTE) 风险预测的准确性. 未来的研究应该专注于多中心研究和实时数据集成,以改善静脉瘤预防和患者的治疗结果.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 临床风险管理临床风险管理
背景情况:
- 静脉血栓塞栓症 (VTE) 是一个重大的临床挑战.
- 目前的VTE风险评估工具在准确性和个性化方面存在局限性.
- 优化VTE预防策略需要先进的预测能力.
研究的目的:
- 系统地审查人工智能 (AI) 在VTE风险评估和管理中的应用.
- 评估人工智能模型对VTE的预测性能.
- 使用AI识别导致VTE的关键风险因素.
主要方法:
- 在VTE风险评估中对AI应用进行了全面的范围审查.
- 在2025年3月10日之前,在主要的中国和英语数据库中进行了文献搜索.
- 数据提取和合成的重点是人工智能模型类型,预测准确性和确定的风险因素.
主要成果:
- 审查中包括了23项研究.
- 人工智能模型显示出优异的VTE风险预测准确性,AUC值在0.740到0.990.
- 确定的关键风险因素包括患者,疾病,治疗,实验室和导管相关的因素.
结论:
- 人工智能通过整合多来源数据,为动态和个性化的VTE风险评估提供了显著的优势.
- 需要进一步的多中心研究来验证AI模型在现实世界临床环境中的实用性.
- 将实时监测数据与人工智能集成,有望改善VTE预防和患者的治疗结果.
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