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Updated: Jan 18, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

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CCLR-DL:一种新的统计和深度学习混合方法,用于特征选择和预测医疗保健需求

Guillem Hernández Guillamet1, Francesc López Seguí2, Josep Vidal Alaball3

  • 1eXiT Research Group, Universitat de Girona (UdG), EPS - Edifici P-IV, Carrer Universitat de Girona, 6, Girona, 17003, Catalunya, Spain; Assistance strategy management. Hospital Germans Trias i Pujol, (ICS), Carretera de Canyet, Badalona, 08916, Catalunya, Spain; Research Group on Innovation, Health Economics and Digital Transformation, Institut Germans Trias i Pujol (IGTP), Cami de les Escoles, Badalona, 08916, Catalunya, Spain.

Computer methods and programs in biomedicine
|September 10, 2025
PubMed
概括

新的全面交叉相关和滞后线性回归深度学习 (CCLR-DL) 框架提高了时间序列预测的准确性和可解释性. 这种混合方法提高了对医疗需求的预测,从而改善了资源配置.

关键词:
深度学习是一种深度学习.选择功能选择功能选择.格兰杰因果关系的原因.卫生需求建模健康需求建模多变量时间序列预测.

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