NS-Assist: Nephrotic Syndrome Assistance System for Pediatric Decision-Making in Pandemic Situations
Source abstract
Abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Digital Mental Health Interventions · Electronic Health Records Systems · Mobile Health and mHealth Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Institute of Mathematics and Computer Science, Abdelhafid Boussouf University Center of Mila, Mila 43000, Algeria
LIRE Laboratory, Abdelhamid Mehri Constantine 2 University, Constantine 25000, Algeria
LISI Laboratory of Intelligent Systems and Informatics, Mila 43000, Algeria
LyRIDS ECE Ecole d’Ingénieurs de Paris, 75015 Paris, France
Department of Computer Science, Abdelhamid Mehri Constantine 2 University, Constantine 25000, Algeria
Faculty of Electrical Engineering, Electronics and Automation, University of Ruse “Angel Kanchev”, 7017 Ruse, Bulgaria
Funding
European Union—NextGenerationEU
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