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Optimizing Healthcare Resource Allocation Using Neural Network Algorithms

EasyChair Preprint no. 13277

15 pagesDate: May 14, 2024


Efficient and effective allocation of healthcare resources is crucial for providing high-quality healthcare services and improving patient outcomes. However, resource allocation in healthcare systems often faces significant challenges due to factors such as limited resources, complex patient needs, and dynamic demand patterns. This abstract presents a novel approach to address these allocation challenges by leveraging neural network algorithms.


Neural networks have gained significant attention in various domains for their ability to learn complex patterns and make accurate predictions. In the context of healthcare resource allocation, neural network algorithms can be harnessed to optimize the allocation process and enhance decision-making. This abstract highlights the key components of the proposed approach and the benefits it offers in overcoming allocation challenges.


First, the abstract discusses the data-driven nature of the approach, emphasizing the importance of comprehensive and accurate data collection. By leveraging large-scale datasets containing information such as patient demographics, medical history, and resource availability, neural network algorithms can learn patterns and relationships that inform resource allocation decisions. This data-driven approach enables healthcare systems to move away from traditional heuristics and manual decision-making, leading to more efficient and informed resource allocation.

Keyphrases: Allocation challenges, Complex patient needs, data-driven approach, Healthcare resource allocation, neural network algorithms, Optimization

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Edwin Frank and Harold Jonathan},
  title = {Optimizing Healthcare Resource Allocation Using Neural Network Algorithms},
  howpublished = {EasyChair Preprint no. 13277},

  year = {EasyChair, 2024}}
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