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Advancing Medicine and Healthcare through Federal Learning

Submission deadline: 30 November 2024
Special Issue Editors
Saurav Mallik
Department of Pharmacology & Toxicology, The University of Arizona, Tucson, AZ, USA
Interests: Computational Biology; Data Mining; Bio-statistics; Machine Learning
Mathivanan Sandeep Kumar
School of Computing Science & Engineering, Galgotias University, Greater Noida, India
Interests: Artificial Intelligence; Block Chain; Computational Biology; Data Mining; Bio-statistics; Machine Learning
S. K.B. Sangeetha
Department of Computer Science and Engineering, SRM Institute of Science and Technology, Vadapalani Campus, Chennai, Tamil Nadu, India.
Interests: Data analytics; Machine Learning; Deep Learning
Sudeshna Rakshit
Department of Biotechnology, SRM Institute of Science and Technology, Kattankulathur, Chennai, Tamil Nadu 603203, India
Interests: Cardio-Oncology; Epigenetics; Immunology
Special Issue Information

In the field of biomedical and health care, machine (federal) learning algorithm has the potential to revolutionize the way medical data is collected, analyzed, and utilized to improve patient outcomes. This proposal proposes to explore the intersection of federated learning, medicine, and health care (neuroscience), with a focus on the opportunities and challenges of integrating these disciplines to advance personalized medicine. We will discuss how machine (federated) learning can be leveraged to improve patient care, predict disease progression, and optimize treatment outcomes by analyzing neural data in a privacy-preserving manner. Additionally, we will highlight future research directions and potential applications of this interdisciplinary approach in the healthcare industry.

Potential areas of interest include, but are not limited to:

  • Collaborative healthcare networks
  • Privacy and security in health data sharing
  • Machine learning applications in healthcare
  • Personalized medicine and treatment
  • Data interoperability in healthcare systems
  • Patient-centered care models
  • Brain-computer interfaces for healthcare
  • Federated learning for remote monitoring and diagnosis
  • Real-time data analytics for healthcare decision-making
  • Machine learning for improving patient outcomes
  • Augmented reality in healthcare training and education
  • Pharmacology and drug discovery using Artificial intelligence.
Federated Learning
Data Security
Neural Networks
Digital Health
Federated Models
Healthcare Solutions
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INNOSC Theranostics and Pharmacological Sciences, Electronic ISSN: 2705-0823 Published by AccScience Publishing