Search
43 articles for “Clinical Decision Support System”
-
Role of W.A.R. Score in Predicting Wound Infection
Abstract: In clinical practice, chronic wounds are a major difficulty that are frequently made worse by the infection risk. However, the lack of precise standards for evaluating the risk of infection and establishing the need for and length of time for systemic antibiotics leads to the overuse and abuse of these drugs, which increases the risk of side effects and the development of antibiotic resistance. To close this gap, the development …
Published in Emerging Trends in Personalized Medicines · Vol. 1, Issue 1, 2024 · pp. 33–38 Read article
-
Impact of Smartphone Use on Health Status and Study Habits Among Students: A Study of Self-Financing Nursing Colleges
Abstract: Background: The proliferation of smartphones within academic environments has introduced far-reaching consequences for both physical and psychological well-being and academic engagement. Nursing students, whose professional formation depends on concentrated learning and sound clinical reasoning, represent a particularly vulnerable yet understudied group within this discourse. Objective: This systematic review synthesizes peer-reviewed evidence examining the multidimensional impact of smartphone use on the health status and study habits of students enrolled in self-financing …
Published in Journal of Nursing Science & Practice · Vol. 16, Issue 2, 2026 · pp. 31–41 Read article
-
A Threshold-Weighted Mathematical Fusion Model for Epidemic Outbreak Prediction Using SEIR Residual Dynamics and Cloud-Based Machine Learning
Abstract: Accurate prediction of epidemic outbreaks is critical for effective public health management, resource planning, early warning generation, and timely intervention by municipal authorities. Traditional compartmental models such as Susceptible–Exposed–Infectious–Recovered (SEIR) offer valuable epidemiological insights and mathematical interpretability; however, they may not adequately capture the complex nonlinear relationships present in real-world urban health systems. Conversely, data-driven machine learning techniques can identify hidden patterns in large datasets but often lack epidemiological structure …
Published in Research & Reviews : Journal of Statistics · Vol. 15, Issue 2, 2026 · pp. 12–19 Read article