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机器学习可解释性方法,以描述血液生物标志物的重要性,预测疑似感染患者的预后

Dipak P Upadhyaya1, Yasir Tarabichi2, Katrina Prantzalos1

  • 1Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, USA.

Computers in biology and medicine
|October 11, 2024
PubMed
概括

当与常规测试相结合时,单细胞分布宽度 (MDW) 并不能显著改善败血症结果预测. 标准的血液学参数和生命体征足以使用机器学习模型准确预测败血症,特别是在资源有限的环境中.

关键词:
功能重要性 功能重要性功能交互效应效应的特征交互效应血液学参数和生命体征生物标志物MDW生物标志物机器学习可以解释机器学习的解释性.败血症查 败血症查

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