Synapse
⌘+K
Synapse
PulseExploreClubsResearchersJournals
Instagram
HomeClubsExplore
September 10, 2025TMP Universal Journal of Research and Review Archives.

Application of Machine Learning Techniques to Mitigate Uncertainty in the Health and Medical Supply Chain

View Full Paper
Ask AI
Bookmark
Share

Authors

EMEngineer Mehdi Afrasiabi MojarradIslamic Azad University, Bushehr Branch

Discussion

Loading...

Member takes

Implication

Platform-based approach forecasts blood demand and reduces wastage in medical supply chains, indicating improvements in collection and distribution.

Key Points

  • An 11% increase in blood collection was achieved using machine learning techniques for forecasting.
  • The approach resulted in a 20% reduction in inventory wastage, improving supply chain efficiency.
  • The AI-driven decision support system identifies key clinical predictors essential for accurate blood demand forecasting.
  • Effective inventory management strategies helped balance blood collection and distribution, minimizing shortages.

Cite This Study

Engineer Mehdi Afrasiabi Mojarrad (2025) studied this question.

synapsesocial.com/papers/68c199e89b7b07f3a061b7dehttps://doi.org/10.69557/ujrra.v4i3.204
View Full Paper
Ask AI
Bookmark
Share

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1AI and Blood Banking: Predicting Transfusion Demand - A Systematic Review of Forecasting Approaches2026
  2. 2Advancing Blood Supply Chain Prediction Based on a Novel Hybrid Machine Learning2026
  3. 3Artificial Intelligence in Patient Blood Management: A Systematic Review of Predictive, Diagnostic, and Decision Support Applications2025 · 6 citations
  4. 4Harnessing big data and artificial intelligence in transfusion medicine: Opportunities for precision, safety and efficiency2026 · 2 citations
  5. 5Transforming Blood Management Systems in Developing Countries Through Technological and Artificial Intelligence-Driven Innovations2026