在败血症中使用机器学习算法
Luisa Agnello1, Matteo Vidali2, Andrea Padoan3
1Institute of Clinical Biochemistry, Clinical Molecular Medicine and Clinical Laboratory Medicine, Department of Biomedicine, Neurosciences and Advanced Diagnostics, University of Palermo, Palermo, Italy.
Clinica chimica acta; international journal of clinical chemistry
|December 29, 2023
概括
机器学习 (ML) 在实验室诊断中显示出早期败血症检测的前景. 进一步标准化ML模型验证和特征定义对于临床实施至关重要.
科学领域:
- 医疗信息学 医疗信息学
- 临床诊断 临床诊断 临床诊断
- 计算生物学 计算生物学
背景情况:
- 败血症对全球健康造成重大负担,死亡率和发病率高.
- 由于可变的临床症状,早期检测败血症具有挑战性.
- 机器学习 (ML) 集成到实验室医学中,为改善败血症识别和预测结果提供了潜力.
研究的目的:
- 综合审查目前关于ML在败血症实验室诊断中的应用的研究.
- 评估现有的ML治疗败血症方法的优点和局限性.
- 确定ML模型开发和临床使用验证的改进领域.
主要方法:
- 在PubMed和Scopus数据库 (关键词:败血症,机器学习,实验室) 中进行广泛的文献搜索,直到2023年1月.
- 两个独立的调查人员选和评估了135篇文章,选择了39篇文章.
- 研究的分析基于设计,意图 (诊断/预后),临床环境,数据,ML方法和验证.
主要成果:
- 大多数包括的研究 (30/39) 专注于ML用于败血症诊断,较少 (8/39) 用于预后.
- 在各种期刊中正在开发ML算法,这表明跨学科的兴趣.
- 在研究设计,特征定义和验证方法方面存在显著差异.
结论:
- 通过实验室数据,ML对提升早期败血症诊断具有相当大的希望.
- 在败血症中,对ML模型的标准化验证协议和特征定义非常需要.
- 标准化对于确保ML工具用于败血症管理的可靠性和临床适用性至关重要.
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