Early and dependable recognition of mental health disorders, which rank among the largest contributors to worldwide disease burden, is central to timely care. Text-based screening tools can assist clinicians, yet most published approaches handle only two classes, draw on a single data source, struggle with skewed class distributions and give clinicians little insight into why a decision was made. We present XplainMentalFusion, an interpretable ensemble of deep learning models that labels each text as neutral or as one of five conditions (anxiety, depression, bipolar disorder, schizophrenia and PTSD). Expert-labelled symptom records, rewritten as short natural-language descriptions, are pooled with Reddit posts to form a heterogeneous corpus. A DistilBERT encoder models contextual meaning, a two-way recurrent BiLSTM layer tracks the sequence in which emotional cues appear, and their class probabilities are averaged through soft voting. SHapley Additive exPlanations (SHAP) reveal, both across the corpus and for single cases, which words push a prediction towards each category. Evaluated on 14,400 stratified held-out samples, the ensemble scored 93.4% on both accuracy and F1 with an AUC of 90.8%, outperforming the logistic regression and random forest baselines as well as either deep network on its own. The findings suggest that pairing heterogeneous clinical and social media text with an explainable ensemble yields balanced, transparent multiclass predictions that could be embedded in clinical decision support and digital mental health screening workflows to assist, not replace, professional judgement.
Roughly a quarter of the global population is anemic, yet laboratory hemoglobin measurement remains hard to obtain in low-resource settings. In this pilot work we introduce AnemiaFusionNet, a deep model that detects anemia by jointly using clinical survey responses, palpebral conjunctiva photographs and location information. A hybrid dataset of 114 participants (74 anemic, 40 non-anemic) from 16 Indian states and union territories was collected through an online survey distributed via WhatsApp and Facebook. Each conjunctival photograph is described in two ways: by convolutional (CNN) features, and by a conjunctiva color-and-pallor-aware (CCP) block that pairs color statistics with GLCM-based texture descriptors. Clinical and geospatial features are encoded separately, and all modalities are fused by a transformer with multi-head self- and cross-attention. Trained on the original 114 samples, the model reached 100% validation accuracy within three epochs, indicating overfitting. After image augmentation and a 70:15:15 split of the augmented dataset, AnemiaFusionNet achieved 92.0% accuracy, 93.5% precision, 89.9% sensitivity, 94.0% specificity and a 91.7% F1-score on 3000 test samples. For comparison, geospatial-only and clinical-only logistic regression reached 70.0% and 88.8% accuracy, and an image-only ResNet-50 reached 100%. Because the augmented samples were split at the sample level, these estimates are likely optimistic; participant-level validation on a larger cohort is required. The study demonstrates the feasibility of survey-based multimodal data collection for anemia screening research.
Class imbalance poses a fundamental challenge in medical image classification, where rare pathologies are severely underrepresented in training datasets. This paper presents ADCL-DHNM (Adaptive Dynamic Contrastive Learning with Dynamic Hard Negative Mining), a novel framework that synergistically combines supervised contrastive learning with an adaptive hard negative mining strategy. Our approach introduces a class-aware contrastive loss with dynamic threshold adaptation based on training progress and class distribution, ensuring proportional representation of minority classes during feature learning. We provide theoretical analysis demonstrating that our adaptive threshold mechanism approximates optimal sampling distributions and that the proposed loss function increases gradient contributions from minority classes. Extensive experiments across four benchmark medical imaging datasets (ISIC 2019, ChestX-ray14, HAM10000, and MIMIC-CXR) demonstrate state-of-the-art performance with AUC improvements of 8.2-9.1% over cross-entropy baselines. Rigorous statistical validation (paired t-tests, effect size analysis, bootstrap confidence intervals) confirms the significance of our results (p < 0.001). Comprehensive stress testing under extreme imbalance (up to 1000:1), label noise (up to 30%), and limited data scenarios validates the robustness of our approach.
Aim: This study aimed to explore the prevalence and determinants of musculoskeletal pain (MSP) among undergraduate medical students and compare the findings across physiotherapy, medicine, and pharmacy faculties. Subject and Methods: A cross-sectional study was conducted from August 2023 to February 2024 among 252 female students at Merit University. A structured questionnaire was used, incorporating the Nordic Musculoskeletal Questionnaire (NMQ) and the Global Physical Activity Questionnaire (GPAQ), to evaluate the presence, location, and severity of MSP. Demographic data, body mass index (BMI), and study habits were also collected. Results: The prevalence of MSP was notably high, particularly in the neck and lower back regions. Female students and physiotherapy students in years beyond the first year reported significantly higher rates of pain. A positive correlation was observed between higher BMI, prolonged poor postures during lectures and study sessions, and increased MSP. However, there was no statistically significant difference in MSP prevalence between physiotherapy and other medical students overall. Conclusion: The high prevalence of musculoskeletal pain among female medical students calls for further ergonomic research and clinical evaluation. Medical schools should address MSP by improving seating arrangements and educating students on lifestyle modifications for MSP prevention.
Sustainable technology development has become a strategic priority for manufacturing firms undergoing digital transformation and facing increasing environmental pressures. Open innovation (OI), particularly inbound knowledge acquisition, plays a crucial role by enabling organizations to integrate diverse technological, scientific, market-based, and digitally mediated knowledge. However, the effectiveness of inbound OI depends on the firm's ability to absorb, analyse, and apply external information, a capability that is increasingly shaped by artificial intelligence (AI). This study investigates how four dimensions of inbound OI (external technology sourcing, university-industry collaboration, customer and market knowledge acquisition, and digital platform engagement) affect sustainable technology development in Saudi Arabian manufacturing firms, and whether AI capability strengthens these effects. Using data collected from 211 IT managers and IT experts and analysed through Smart PLS 4, the results show that all four inbound OI dimensions significantly enhance sustainable technology development. AI capability moderated the relationship between inbound OI and sustainability outcomes. These findings enrich OI theory by introducing a multidimensional conceptualization and highlight AI capability as a strategic enabler of environmental technological progress. The study offers actionable insights for managers and policymakers seeking to accelerate sustainable industrial transformation.
