CRITICAL ANALYSIS ON STRENGTHENING PUBLIC HEALTH SURVEILLANCE THROUGH INTERDISCIPLINARY COLLABORATION BETWEEN EPIDEMIOLOGICAL TECHNICIANS AND HEALTH INFORMATICS SPECIALISTS. DOI Creative Commons

Hamad Nahi Hamad Alshamlan,

Khaled Ali Bin Khazim Alshehri,

Khlood Ali Yahya Asiri

et al.

Published: Jan. 1, 2022

Effective public health surveillance is basic to rapidly distinguish and react developing well-being issues. This article analyzes the benefits suggestions of collaboration between epidemiologists available specialists move forward with administrations. Through a subjective writing survey investigation strategies, comes about, discoveries, this investigates each discipline's commitment moving assessment. Key discoveries are examined, counting challenges openings recognized in past thinks about assessments. Furthermore, proposals were made progress professionals outcomes.

Language: Английский

Exploring the Impact of Artificial Intelligence on Healthcare Management: A Combined Systematic Review and Machine-Learning Approach DOI Creative Commons
Vito Santamato, Caterina Tricase, Nicola Faccilongo

et al.

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(22), P. 10144 - 10144

Published: Nov. 6, 2024

The integration of artificial intelligence (AI) in healthcare management marks a significant advance technological innovation, promising transformative effects on processes, patient care, and the efficacy emergency responses. scientific novelty study lies its integrated approach, combining systematic review predictive algorithms to provide comprehensive understanding AI’s role improving across different contexts. Covering period between 2019 2023, which includes global challenges posed by COVID-19 pandemic, this research investigates operational, strategic, response implications AI adoption sector. It further examines how impact varies temporal geographical addresses two main objectives: explore influences domains, identify variations based Utilizing an we compared various prediction algorithms, including logistic regression, interpreted results through SHAP (SHapley Additive exPlanations) analysis. findings reveal five key thematic areas: enhancing quality assurance, resource management, security, pandemic. highlights positive influence operational efficiency strategic decision making, while also identifying related data privacy, ethical considerations, need for ongoing integration. These insights opportunities targeted interventions optimize current future landscapes. In conclusion, work contributes deeper provides policymakers, professionals, researchers, offering roadmap addressing both

Language: Английский

Citations

16
Investigating Emerging Trends in Sustainable Fashion Research: Topics, Challenges, Strategies, and Future Directions DOI
Sher Singh Yadav, Sanjay Kumar Kar, Shrawan Kumar Trivedi

et al.

IEEE Transactions on Engineering Management, Journal Year: 2024, Volume and Issue: 71, P. 5600 - 5615

Published: Jan. 1, 2024

Apparel industry is grappling with sustainability challenges. Research in this domain has seen significant growth over the past five years. However, a clear division of topics within field found lacking. This study addresses gap by examining intersection fashion and sustainability, assessing its evolution, characteristics potential research topics. The aims to assess literature on fashion, ascertain trends provide future directions as well implications. Exhaustive search was conducted using comprehensive database Scopus considering studies before February 2023, earliest having been published 1963. resulting sample 658 articles analysed topic modelling method text mining R software Latent Dirichlet Allocation covering various sustainable from manufacturing marketing. Results analysis revealed that purchase intention behaviour are at top output. We present strategies challenges for implementing circular economy sector. study, best our knowledge, first apply approach examination literature. It offers interdisciplinary assessment research.

Language: Английский

Citations

10
A Diabetes Mellitus Detection Using Fusion of IoMT, Generative AI, and eXplainable AI DOI

G Varun,

S. Sarveswaran,

S. Shreeshaa

et al.

Advances in healthcare information systems and administration book series, Journal Year: 2025, Volume and Issue: unknown, P. 291 - 322

Published: Jan. 17, 2025

Diabetes Mellitus (DM) is a metabolic disorder when the sugar level in blood elevated consistently. The presence of one global health challenges, several research works focusing on early detection and management innovative machine learning technologies were developed recent years. In this book chapter, we introduce novel approach to classify diabetes mellitus by leveraging Internet Medical Things (IoMT) generative AI models. IoT devices continuously monitor critical data transmit them central model for analysis preprocessing done. preprocessed act as input models predict diabetes. imbalanced dataset converted into balanced using two called VAE GAN. We used five ML classification kNN, SVM, DT, LR RF with boosting. Hard voting performed determine final class. Our experiment result shows that proposed ensemble produces an accuracy 81% which outperformed other model's

Language: Английский

Citations

0
Global trends in sustainable healthcare research: a bibliometric analysis DOI Creative Commons

Dr Ana Raquel Nunes,

Phillip J. Dale

Future Healthcare Journal, Journal Year: 2025, Volume and Issue: unknown, P. 100251 - 100251

Published: April 1, 2025

Language: Английский

Citations

0
Emerging dimensions in Fintech: Insights from bibliometric analysis DOI Creative Commons
Mahak Sethi, Narendra Singh Bohra, Amar Johri

et al.

Digital Business, Journal Year: 2025, Volume and Issue: unknown, P. 100113 - 100113

Published: Feb. 1, 2025

Language: Английский

Citations

0
A smart inventory management system with medication demand dependencies in a hospital supply chain: A multi-agent reinforcement learning approach DOI
Esha Saha, Pradeep Rathore

Computers & Industrial Engineering, Journal Year: 2024, Volume and Issue: 191, P. 110165 - 110165

Published: April 18, 2024

Language: Английский

Citations

2
Greening Healthcare: Exploring Economic and Ecological Synergies in the Pursuit of Net-Zero Carbon Futures DOI Creative Commons
Mohd Afjal, Jatin Trivedi

Sustainable Futures, Journal Year: 2024, Volume and Issue: 8, P. 100381 - 100381

Published: Nov. 17, 2024

Language: Английский

Citations

2
Challenges and Opportunities in Energy Economics: Balancing Cost, Sustainability, and Innovation in the Global Energy Transition DOI Open Access
Nasr Bensalah

Modern Economy, Journal Year: 2024, Volume and Issue: 15(11), P. 1147 - 1180

Published: Jan. 1, 2024

Language: Английский

Citations

2
A multi-appointment patient scheduling system with machine learning and optimization DOI Creative Commons
Ying Han, Marina Johnson, Xiaojun Shan

et al.

Decision Analytics Journal, Journal Year: 2023, Volume and Issue: 10, P. 100392 - 100392

Published: Dec. 27, 2023

Appointment scheduling is critical to increasing resource utilization and operational performance in various industry domains, especially healthcare. Costs care for several serious diseases are projected grow due the aging population rising drug prices. Thus, there an urgent need efficient planning reduce expenses. This research explores ways effectively schedule outpatient chemotherapy visits with multiple appointments requiring different resources. The study aims assess impact of patient no-shows individual stochastic appointment durations determine if overbooking viable mitigate adverse effects no-shows. first applies artificial neural networks (ANN) calculate no-show probabilities individualized durations. Then, it builds optimization models that use ANN models' outcomes visits. schedules obtained from these assessed using simulation analysis identify effectiveness combat produce better key indicators.

Language: Английский

Citations

5
Data Mining Analysis for Improving Decision-Making in Computer Management Information Systems DOI Open Access

Xiaohong Dong,

Bing Xiang

Scalable Computing Practice and Experience, Journal Year: 2023, Volume and Issue: 24(4), P. 673 - 685

Published: Nov. 17, 2023

In this paper a decision tree-based data mining procedure for information systems is proposed to enhance the accuracy and efficiency of mining. An enhanced C4.5 tree method based on cosine similarity suggested evaluate gain rate characteristics entropy their values. When variance among any two values attributes within threshold range, merging attribute determined, recalculated. Large-scale sets that conventional processing methods are unable handle successfully have given rise area The prime objective look into how technology used in computer management systems. benefits technologies examined from variety angles study. order analyze comprehend huge derive knowledge can be utilized decision-making process systems, solution makes use number techniques, including Clustering, Classification, Association Rule Mining. experimental analysis indicates time required by construct less than GBDT, P-GBDT C5.0 Hyperion image forest type fine classification method. minimum not more 15 seconds when compared with saving other methods. always greatest comparison tree. various datasets exceeds 95 percent, efficacy high. This enhances precision uncover valuable concealed behind large volume maximize its value.

Language: Английский

Citations

1