Special Issues
Table of Content

AI-Based Tools for Precision Medicine Solutions

Submission Deadline: 15 November 2023 (closed) View: 119

Guest Editors

Prof. V.Vijayakumar, UNSW Australia
Dr. Małgorzata Kujawska, Poznan University of Medical Sciences, Poland

Summary

Precision medicine (PM) is a medical concept providing personalized medical care. It refers to the diagnosis, treatment, and prevention of various diseases tailored to a specific subgroup of patients. The development of PM is driven by the development of the high-throughput omics-related biomarkers, wireless monitoring, and pharmaco-omics-based drug development programs alongside Artificial intelligence (AI) tools. AI augments the design of treatment plans through assistance in medication management or drug and biomarkers discovery. 

The Discovery of omics-related biomarkers enables personalizing early detection strategies and personalizing disease prevention and, together with wireless monitoring, are applied in treatment management. The development of in silico modeling technologies and tools for virtual screening is applied in designing optimal pharmacokinetic and pharmacodynamic properties of personalized medicines. 

 

Next-generation PM strategies deal with applying patient-derived cells or organoids to predict disease, design optimal therapies, and develop personalized digital therapeutics. 

 

Numerous challenges associated with ethics, security of personal data, data integration, human resources to manage the data, and algorithms, however, still exist to be addressed in the future of PM. 

 

In this Special Issue, we call for innovative use of advanced AI-based approaches in PM solutions. Research and review papers covering these topics are invited for this Special Issue, especially those with translational value. 

 

The addressed topics include (but are not limited to): 

· drug, vaccines, and biomarkers discovery 

· pharmacotherapy optimization 

· Point-of-care testing and clinical decision making 

· “disease in a dish” -  cellular or organoid model of a disease condition 

· digital therapeutics 

· personal data security management 


Keywords

Omics technology 
Avatars 
Machine learning 
Artificial Intelligence 
m-Health 
e-Health 
wearable technology 
digital health 
security 

Published Papers


  • Open Access

    ARTICLE

    Improving Prediction of Chronic Kidney Disease Using KNN Imputed SMOTE Features and TrioNet Model

    Nazik Alturki, Abdulaziz Altamimi, Muhammad Umer, Oumaima Saidani, Amal Alshardan, Shtwai Alsubai, Marwan Omar, Imran Ashraf
    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3513-3534, 2024, DOI:10.32604/cmes.2023.045868
    (This article belongs to the Special Issue: AI-Based Tools for Precision Medicine Solutions)
    Abstract Chronic kidney disease (CKD) is a major health concern today, requiring early and accurate diagnosis. Machine learning has emerged as a powerful tool for disease detection, and medical professionals are increasingly using ML classifier algorithms to identify CKD early. This study explores the application of advanced machine learning techniques on a CKD dataset obtained from the University of California, UC Irvine Machine Learning repository. The research introduces TrioNet, an ensemble model combining extreme gradient boosting, random forest, and extra tree classifier, which excels in providing highly accurate predictions for CKD. Furthermore, K nearest neighbor (KNN) More >

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