人工智能模型在健康信息交换中:对临床影响的系统审查
Sahar Borna1, Michael J Maniaci2, Clifton R Haider3
1Division of Plastic Surgery, Mayo Clinic, Jacksonville, FL 32224, USA.
人工智能 (AI) 模型显示出强大的预测能力,使用健康信息交换 (HIE) 数据来预测临床结果. 需要进一步的研究,以解决可靠的人工智能增强医疗保健的敏感度变化.
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
- 临床数据分析
背景情况:
- 电子健康记录 (EHR) 系统对于患者数据管理至关重要.
- 健康信息交换 (HIE) 标准化和整合患者文件,以改善护理.
- 在EHR中人工智能应用得到了充分的研究,但在HIE中人工智能仍未得到充分探索.
研究的目的:
- 系统地审查AI模型在健康信息交换 (HIE) 中的应用.
- 评估预测性能,并确定AI在HIE中的局限性.
- 为了解决AI在临床决策中的HIE中的作用的差距.
主要方法:
- 系统的文献审查遵循PRISMA指南.
- 搜索了包括Scopus,CINAHL,谷歌学者,PubMed/Medline和科学网络在内的数据库.
- 分析了从最初的1021人中选出的11个出版物.
主要成果:
- 机器学习模型主要用于预测临床结果,特别是在瘤学和心脏病方面.
- 人工智能模型显示出显著的预测准确性,AUC值在61%至99.91%之间.
- 性能指标显示出变化,灵敏度在12%至96.50%之间,特异性在76.30%至98.80%之间.
结论:
- 利用HIE数据的AI模型对临床判断具有相当大的预测能力.
- 人工智能与HIE的整合对医疗保健具有变革性的潜力.
- 解决人工智能模型灵敏度的变化对于开发可靠的人工智能增强医疗保健解决方案至关重要.
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