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Deep Learning for Personalized Health Monitoring and Prediction: A Review
  • +3
  • Robertas Damaševicˇius,
  • Senthil Kumar Jagatheesaperumal,
  • Rajesh Kandala N V P S,
  • Sadiq Hussain,
  • Roohallah Alizadehsani,
  • Jua Gorriz
Robertas Damaševicˇius
Vytauto Didziojo Universitetas Viesosios Komunikacijos Katedra
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Senthil Kumar Jagatheesaperumal
Mepco Schlenk Engineering College
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Rajesh Kandala N V P S
VIT-AP Campus

Corresponding Author:kandala.rajesh2014@gmail.com

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Sadiq Hussain
Dibrugarh University
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Roohallah Alizadehsani
Deakin University - Geelong Waurn Ponds Campus
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Jua Gorriz
Universidad de Granada Departamento de Ciencias de la Computacion e Inteligencia Artificial
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Abstract

Personalized health monitoring and prediction have become essential for improving health- care delivery, especially with the growing prevalence of chronic diseases and an aging population. Deep learning (DL) has emerged as a promising approach for developing personalized health mon- itoring systems that can predict health outcomes accurately and efficiently. With the increasing availability of personal health data, DL-based methods have emerged as a promising approach to improve healthcare delivery by providing accurate and timely predictions of health outcomes. This article provides a comprehensive review of the recent developments in the application of DL for personalized health monitoring and prediction. It summarizes various DL architectures and their applications for personalized health monitoring, including wearable devices, electronic health records, and social media data. Furthermore, the article also explores the challenges and future directions for the application of DL in personalized health monitoring. valuable insights into the potential of DL for personalized health monitoring and prediction.
20 Jun 2023Submitted to Computational Intelligence
20 Jun 2023Submission Checks Completed
20 Jun 2023Assigned to Editor
21 Jun 2023Review(s) Completed, Editorial Evaluation Pending
15 Apr 2024Submission Checks Completed
15 Apr 2024Assigned to Editor
21 Apr 2024Reviewer(s) Assigned
01 May 2024Review(s) Completed, Editorial Evaluation Pending
09 May 2024Editorial Decision: Accept