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Related Concept Videos

Issues And Trends In Healthcare Delivery System01:29

Issues And Trends In Healthcare Delivery System

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The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Applied convolutional neural network framework for tagging healthcare systems in crowd protest environment.

Gaurav Tripathi1, Kuldeep Singh2, Dinesh Kumar Vishwakarma3

  • 1Department of ECE, Delhi Technological University, Delhi 110042, India.

Mathematical Biosciences and Engineering : MBE
|November 24, 2021
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Summary

This study introduces a deep learning system for smart healthcare in crowded public spaces. It accurately tags injured individuals during protests, optimizing emergency response and specialist allocation.

Keywords:
convolutional neural networkdeep learninghealthcareinternet of thingsprotestviolence detection

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Area of Science:

  • Smart City Infrastructure
  • Public Health Surveillance
  • Artificial Intelligence in Healthcare

Background:

  • Smart healthcare systems are crucial for urban infrastructure, focusing on patient monitoring and proactive public health measures.
  • Managing crowds and public safety, especially during protests, presents significant challenges in smart cities.
  • Existing systems lack efficient mechanisms for real-time healthcare tagging and resource allocation during mass gatherings.

Purpose of the Study:

  • To develop a novel deep learning-based decision support system for Internet of Things (IoT) enabled smart healthcare.
  • To create a system for tagging injured individuals during crowd protests and violent events in mass gatherings.
  • To classify protest severity and optimize the allocation of specialist healthcare professionals for emergency response.

Main Methods:

  • Utilized a deep learning approach within an Internet of Things (IoT) environment.
  • Developed a decision support system for classifying protest behaviors (normal, medium, severe).
  • Implemented a healthcare tagging mechanism for injured individuals in crowded and protest scenarios.

Main Results:

  • The system achieved over 81% accuracy in classifying protest attributes.
  • Demonstrated over 90% accuracy in differentiating between protests and violent actions.
  • Successfully demonstrated the feasibility of real-time external surveillance and healthcare tagging for emergency response.

Conclusions:

  • The proposed deep learning system offers an optimized solution for smart healthcare in mass gathering environments.
  • The system effectively tags and allocates specialist healthcare professionals during emergencies in crowded public spaces.
  • Results indicate strong potential for real-time application in external surveillance and healthcare tagging during public events.