Wang Hongyan, Liao Yuehui, Gao Li, Li Panfei, Huang Junwei, Xu Peng, Fu Bin, Zhu Qin, Lai Xiaobo
School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou 310053, China.
Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou 310005, China.
Sensors (Basel). 2025 Mar 19;25(6):1916. doi: 10.3390/s25061916.
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.
MAL-Net integrates Long Short-Term Memory (LSTM) networks with Multi-Head Attention (MHA) mechanisms to effectively capture sequential and contextual dependencies in clinical data. A memory network module extracts features from clinical sensors and records, while the MHA module emphasizes critical features and mitigates class imbalance. The model was trained and validated on clinical data from 500 IgAN patients, incorporating demographic, laboratory, and symptomatic variables. Performance was evaluated against six baseline models, including traditional machine learning and deep learning approaches.
MAL-Net outperformed all baseline models, achieving 91% accuracy and an AUC of 0.97. The integration of MHA significantly enhanced classification performance, particularly for underrepresented subtypes. The F1-score for the Ni-du subtype improved by 0.8, demonstrating the model's ability to address class imbalance and improve precision.
MAL-Net provides a robust solution for multi-label IgAN subtype classification, tackling challenges such as data heterogeneity, class imbalance, and feature interdependencies. By integrating clinical sensor data, MAL-Net enhances IgAN subtype prediction, supporting early diagnosis, personalized treatment, and improved prognosis evaluation.
IgA肾病（IgAN）是肾衰竭的主要原因，其临床和病理表现具有显著异质性。由于临床表现重叠和数据的多维度性质，准确的亚型分类仍然具有挑战性。传统方法往往无法充分捕捉IgAN的复杂性，限制了它们的临床适用性。本研究引入了MAL-Net，这是一种用于IgAN亚型多标签分类的深度学习框架，利用多维临床数据并纳入基于传感器的输入，如实验室指标和症状跟踪。
MAL-Net将长短期记忆（LSTM）网络与多头注意力（MHA）机制相结合，以有效捕捉临床数据中的序列和上下文依赖性。一个记忆网络模块从临床传感器和记录中提取特征，而MHA模块强调关键特征并减轻类别不平衡。该模型在来自500例IgAN患者的临床数据上进行训练和验证，纳入了人口统计学、实验室和症状变量。与六个基线模型进行性能评估，包括传统机器学习和深度学习方法。
MAL-Net优于所有基线模型，准确率达到91%，曲线下面积（AUC）为0.97。MHA的整合显著提高了分类性能，特别是对于代表性不足的亚型。Ni-du亚型的F1分数提高了0.8，证明了该模型解决类别不平衡和提高精度的能力。
MAL-Net为IgAN亚型多标签分类提供了一个强大的解决方案，应对了数据异质性、类别不平衡和特征相互依赖性等挑战。通过整合临床传感器数据，MAL-Net增强了IgAN亚型预测，支持早期诊断、个性化治疗和改善预后评估。
