Special Issue on Impact of Machine Learning on Personalized Medicine in Public Health Submission Date: 2024-11-20 The integration of advanced analytics and healthcare has opened new horizons in adapting medical measures to

individual patients revolutionizing the field of personalized medicine. Normally, machine learning algorithms have the

capacity to analyse vast amounts of diverse patient data. These data may include genetic information, clinical records,

lifestyle factors and other essential data. Using the algorithms healthcare professionals can extract invaluable insights

and patterns from the data providing customized solutions based on individual characteristics and needs. Machine

learning also plays an important role in precision diagnostics and treatment recommendations. It has the capabilities to

analyse patient characteristics, genetic factors, and treatment responses and predict drug reactions, adverse events and

optimal dosage levels. This enables healthcare providers to suggest medicines with minimized side effects by

improving more therapeutic outcomes. Moreover, the application of machine learning in public health will provide

more accurate prediction in infectious disease surveillance and outbreak. Further, machine learning algorithms can also

detect early warning signs of disease outbreaks by analysing multiple data sources such as social media feeds, climate

data and geographical information. This will enable public health officials to carry out timely interventions thereby

mitigating the impact of infectious diseases on population health. From disease prediction to outbreak surveillance,

machine learning has the capacity to reshape the landscape of public health, enabling evidence-based decision-making

and ultimately improving health outcomes for individuals and communities. This special issue aims to attract original

research papers from professionals in this domain area to provide insights that can shape the future of personalized

medicine and improve public health outcomes.

Potential topics include, but are not limited to:

• Machine learning approaches for optimizing precision medicine treatment plans

• Genomic data analysis and machine learning for drug response prediction

• Machine learning in identifying genetic markers for targeted therapies

• Applications of machine learning in predicting treatment outcomes and adverse reactions

• Enhancing interpretability and transparency of machine learning models in healthcare

• Overcoming bias and improving fairness in machine learning-based personalized medicine

• Ethical considerations and privacy protections in the era of machine learning and personalized medicine

• Data sharing and governance frameworks for effective collaboration in public health

• Machine learning for early detection and intervention in infectious disease outbreaks

• Automation of public health surveillance systems using machine learning

• Machine learning-enabled decision support systems for healthcare providers

• Real-time monitoring and prediction of public health indicators using machine learning

• Machine learning for optimizing clinical trial design and patient selection in drug development.

Guest Editors

Shadi Mahmoud FalehAlZu’bi, Al-Zaytoonah University of Jordan, dr.shadi.alzubi@gmail.com

MaysamAbbod, Brunel University London, Uxbridge, maysam.abbod@brunel.ac.uk

Ashraf Darwish, Helwan University, Cairo, ashraf.darwish.eg@ieee.org)

Key Dates

Deadline for Submission: 20 Nov, 2024

First Reviews Due: 25 Jan, 2025

Revised Manuscript Due: 30 Mar, 2025

Final Decision: 20 May, 2025