Major Depressive Disorder (MDD) is a prevalent mental illness and a leading cause of suicide, making early detection critical. The electroencephalogram (EEG) provides a non-invasive method for recording brain activity; however, current diagnostic methods rely on manual evaluation by neurologists, which can be subjective, labor-intensive, and time-consuming. While Machine Learning (ML) and Deep Learning (DL) have shown promise in medical diagnosis, many existing models for MDD detection lack generalizability due to a large number of learnable parameters, small datasets, and data leakage from Patient-Independent evaluation protocols. In this work, we propose an ensemble classifier of lightweight convolutional neural networks (CNNs) for MDD detection using EEG signals, designed with minimal learnable parameters to ensure efficiency and generalization. The method was evaluated on the MUMTAZ benchmark dataset using Leave-Some-Subject-Out Cross Validation (LSSOCV) to ensure robust performance, achieving an accuracy of 94.1
This study investigates the topological differences between functional networks in the left and right hemispheric regions during visual word processing using task-based fMRI data from 45 subjects in the OpenfMRI project. The analysis employs Automated Anatomical Labeling (AAL) atlases to construct functional networks under two experimental conditions: word processing and consonant string processing. Four key topological metrics are systematically compared between hemispheres: degree centrality, global efficiency, clustering coefficient, and local efficiency. Significant hemispheric differences in local efficiency occurred between 13–19 ≤ 1 ≤ 1 ≤ 4
Hotline text data, characterized by their colloquial, fragmented, and noisy nature, present significant challenges for traditional event extraction methods based on formal written text. To address these challenges, this study proposes EENE, a Graph-Aware Transformer model that reformulates multi-stage hotline event extraction as a graph- structured decoding task centered on trigger words to achieve efficient event extraction from fragmented and noisy hotline texts. By integrating BERT-based semantic encoding with Transformer-based dependency modeling, EENE effectively captures long-range contextual dependencies and fine-grained semantic associations for accurately extracting token–token association from fragmented and noisy texts. Furthermore, these connections are utilized to construct a token–token association matrix in which triggers serve as central nodes and arguments as edge nodes, thereby enabling event extraction by structured graph reasoning. Multi-stage event extraction is thus transformed into a single-step association extraction task, effectively avoiding cascading errors and improving the accuracy of event extraction. Experimental results on two self-built Chinese hotline datasets covering urban flooding, public complaints, and help requests demonstrate that EENE achieves state-of-the-art performance, surpassing representative baselines such as EDEE and CASEE by 5.15
For any machine learning or deep learning model, explainability is of paramount importance. As data complexity increases, deep learning models renowned for their outstanding performance have been applied to a wide range of tasks. However, these models remain typical “black boxes” whose predictions are difficult to interpret. To address this limitation, this study proposes a progressive framework that integrates feature engineering with knowledge distillation for interpreting deep learning-based hypertension prediction models. Statistical relevance testing are performed to select target-associated variables, and numeric variables are first partitioned with a supervised decision tree to obtain interval-based, clinically legible features with interpretable thresholds. On this basis, FT-Transformer is adopted as the teacher to generate soft labels that capture nonlinear structure for distillation. Finally, a globally interpretable linear model is trained on the transformed features and soft labels, preserving accuracy while delivering quantitative, interval-level risk attribution. Experiments on the public MIMIC-III demonstrate that our method significantly outperforms traditional interpretable models and machine learning baselines. Although AUC and accuracy slightly lag behind deep learning models, our approach delivers intuitive feature interpretations while maintaining competitive performance.
