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. Methods: 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. Results: 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. Conclusions: 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.

The development of drugs for ischemic stroke has long been plagued by the dilemma of high investment, high attrition, and low translational success rates. This narrative review summarizes the application of artificial intelligence (AI) in ischemic stroke drug design, specifically encompassing intelligent target mining, the generation and optimization of lead compounds, early prediction of pharmacokinetics/toxicity (ADMET), and the optimization of clinical trial protocols. Although AI-aided drug design has progressed from a theoretical concept to practical validation, significant challenges remain in data quality, model interpretability, and clinical translation. However, with ongoing efforts such as the construction of high-quality multimodal databases and the advancement of explainable AI models, artificial intelligence holds considerable promise for continuously driving the discovery of novel therapeutics for stroke treatment.