机器学习模型识别和预测患者需要ICU入院的需求:系统性审查
Yujing Chen1, Han Chen2, Qian Sun1
1The Eighth Clinical Medical College, Guangzhou University of Chinese Medicine, Foshan, Guangdong, China.
The American journal of emergency medicine
|September 11, 2023
概括
机器学习 (ML) 模型在预测急诊室 (ED) 选期间的重症监护需求方面表现有前途. 需要进一步的研究来验证这些预测工具对重症监护.
科学领域:
- 紧急医疗 紧急医疗
- 人工智能的人工智能
- 临床信息学 临床信息学
背景情况:
- 急诊室 (ED) 的分类对患者的治疗结果至关重要.
- 准确预测重症监护需求会影响生存和预后.
研究的目的:
- 系统地审查机器学习 (ML) 模型在预测ED患者进入重症监护室 (ICU) 的性能.
- 评估各种ML算法的诊断准确性和预测能力.
主要方法:
- 在四个主要数据库 (PubMed,Embase,Cochrane Library,Web of Science) 进行了系统的文献搜索,截至2023年4月28日.
- 预测模型研究偏差风险评估工具 (PROBAST) 用于评估研究质量和模型偏差风险.
主要成果:
- 十项研究使用了算法,包括梯度增强,物流回归,神经网络,支持向量机器和随机森林.
- 性能指标在不同模型中有所不同,梯度提升和物流回归显示了灵敏度,特异性,准确性,精度和AUC的广泛范围.
- 支持向量机器表现出高特异性,而随机森林在某些指标上表现出强的表现.
结论:
- 机器学习模型在识别需要ED重症监护的患者方面具有相当大的潜力.
- 虽然有希望,但研究数量有限,需要高质量的前性研究来进行强有力的验证.
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