MAL-Net: A Multi-Label Deep Learning Framework Integrating LSTM and Multi-Head Attention for Enhanced Classification of IgA Nephropathy Subtypes Using Clinical Sensor Data.

Journal: Sensors (Basel, Switzerland)
PMID: 40293045

Abstract

BACKGROUND: IgA nephropathy (IgAN) is a leading cause of renal failure, characterized by significant clinical and pathological heterogeneity. Accurate subtype classification remains challenging due to overlapping clinical manifestations and the multidimensional nature of data. Traditional methods often fail to fully capture IgAN's complexity, limiting their clinical applicability. This study introduces MAL-Net, a deep learning framework for multi-label classification of IgAN subtypes, leveraging multidimensional clinical data and incorporating sensor-based inputs such as laboratory indices and symptom tracking.

Authors

  • Hongyan Wang
    State Key Laboratory of Genetic Engineering, School of Life Sciences, Zhongshan Hospital, Fudan University, Shanghai, 200432, China.
  • Yuehui Liao
    School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou 310053, China.
  • Li Gao
    College of Veterinary Medicine, Northeast Agricultural University, Harbin 150000, China.
  • Panfei Li
    School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou 310053, China.
  • Junwei Huang
    School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou 310053, China.
  • Peng Xu
    Department of Urology, Zhujiang Hospital, Southern Medical University, Guangzhou, China.
  • Bin Fu
    Department of Orthopaedic Surgery, Changzhou Wujin People's Hospital, Changzhou 213100, China.
  • Qin Zhu
    Division of Humanities, Arts and Social Sciences, Colorado School of Mines, Golden, USA. qzhu@mines.edu.
  • Xiaobo Lai
    College of Medical Technology, Zhejiang Chinese Medical University, Hangzhou 310053, China.

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