Pandemics place extraordinary pressure on healthcare systems, particularly in identifying and prioritizing high-risk groups such as the elderly, pregnant women, and individuals with chronic diseases. Existing Artificial Intelligence models often fall short, focusing on single diseases, lacking interpretability, and overlooking patient-specific vulnerabilities. To address these gaps, we propose an Explainable Deep Learning approach for identifying at-risk patients during pandemics (AI-RiskX). AI-RiskX performs risk classification of patients diagnosed with COVID-19 or related infections to support timely intervention and resource allocation. Unlike previous models, AI-RiskX integrates five public datasets (asthma, diabetes, heart, kidney, and thyroid), employs the Synthetic Minority Over-sampling Technique (SMOTE) for class balancing, and uses a hybrid convolutional neural network–long short-term memory model (CNN–LSTM) for robust disease classification. SHAP (SHapley Additive exPlanations) enables both individual and population-level interpretability, while a post-prediction rule-based module stratifies patients by age and pregnancy status. Achieving 98.78% accuracy, AI-RiskX offers a scalable, interpretable solution for equitable classification and decision support in public health emergencies.

The COVID-19 pandemic has underscored the need for telemedicine to ensure continuity of pediatric care during health emergencies. This paper presents NS-Assist, a hybrid web–mobile decision support system for managing Idiopathic Nephrotic Syndrome (INS) in children. The system combines rule-based reasoning and fuzzy inference to assist clinicians in diagnosis, treatment adjustment, and relapse monitoring, while enabling caregivers to record and track daily health data. Implemented using Spring Boot, ReactJS, and Flutter with a secure MySQL database, NS-Assist integrates medical expertise with computational intelligence to support remote decision-making. A pilot evaluation involving 40 participants, including clinicians and caregivers, showed improved communication, reduced consultation time, and enhanced follow-up continuity. These results highlight the system’s potential as a reliable and adaptable framework for pediatric telemedicine in resource-constrained and emergency settings.