机器学习和后勤回归模型的比较,用于预测电子健康记录中的30天计划外再接收:一项开发和验证研究

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Updated: Jun 16, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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Published on: January 11, 2020

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Masao Iwagami1,2,3,4, Ryota Inokuchi1,2,5,6, Eiryo Kawakami7,8

  • 1Department of Health Services Research, Institute of Medicine, University of Tsukuba, Tsukuba, Ibaraki, Japan.

PLOS digital health
|August 20, 2024
PubMed
概括

梯度增强决策树 (GBDT) 在预测非计划性医院再入院方面,通常优于LASSO (LR-LASSO) 的后勤回归. 然而,随着更多的变量和包括血液检测结果,模型性能差异缩小了.

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