机器学习在心脏外科手术:一个叙事审查
Travis J Miles1,2, Ravi K Ghanta1,2
1Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, TX, USA.
Journal of thoracic disease
|May 13, 2024
概括
机器学习 (ML) 在心脏外科手术中显示出有前途的预测结果,但其临床实用性仍然有限. 将ML与电子健康记录整合起来,可以增强决策支持和改善患者护理.
科学领域:
- 心血管外科心血管外科
- 医疗信息学 医疗信息学
- 人工智能的人工智能
背景情况:
- 机器学习 (ML) 越来越多地应用于医疗挑战.
- 在心脏手术中,ML用于风险分层和结果预测.
- 在该领域,ML的临床实用性需要进一步澄清.
研究的目的:
- 审查心脏外科手术中的ML应用.
- 评估ML在预测分析中的实用性.
- 探索ML对临床决策支持的影响.
主要方法:
- 自2000年以来,PubMed索引文章的叙事审查.
- 搜索术语包括ML,监督ML,深度学习,AI,心血管手术和胸部手术.
主要成果:
- ML准确地预测心脏手术的临床结果,特别是手术前的风险概况.
- 与传统的风险指标相比,这些指标的改进很小.
- 目前ML的临床应用仍然有限.
结论:
- 机器学习擅长处理大量的多维数据,如电子健康记录.
- 在ICU数据上训练的模型显示出强大的预测性能.
- 将ML集成到EHR中可以创建动态决策支持,以改善心脏手术结果.
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