A bidirectional reasoning approach for blood glucose control via invertible neural networks.

Journal: Computer methods and programs in biomedicine
Published Date: May 27, 2025

Abstract

BACKGROUND AND OBJECTIVE: Despite the profound advancements that deep learning models have achieved across a multitude of domains, their propensity to learn spurious correlations significantly impedes their applicability to tasks necessitating causal and counterfactual reasoning.

Authors

  • Jingchi Jiang
    School of Computer Science and Technology, Harbin Institute of Technology, Integrated Laboratory Building 803, Harbin 150001, China. Electronic address: jiangjingchi0118@163.com.
  • Rujia Shen
    Faculty of Computing, Harbin Institute of Technology, Harbin, China.
  • Yang Yang
    Department of Gastrointestinal Surgery, The Third Hospital of Hebei Medical University, Shijiazhuang, China.
  • Boran Wang
    Faculty of Computing, Harbin Institute of Technology (Shenzhen), Shenzhen, China.
  • Yi Guan
    School of Computer Science and Technology, Harbin Institute of Technology, Integrated Laboratory Building 803, Harbin 150001, China. Electronic address: guanyi@hit.edu.cn.

Keywords

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