Medicine and Health

Integrating AI/ML Models for Patient Stratification Leveraging Omics Dataset and Clinical Biomarkers from COVID-19 Patients: A Promising Approach to Personalized Medicine

B. Bello, Y. N. Bundey, et al.

This study conducted by Babatunde Bello, Yogesh N Bundey, Roshan Bhave, Maksim Khotimchenko, Szczepan W Baran, Kaushik Chakravarty, and Jyotika Varshney explores how AI and machine learning can stratify COVID-19 patients. By analyzing omics data and clinical biomarkers, high-accuracy models were developed to predict severity and survival, revealing essential biomarkers linked to severe cases and survival rates. This research emphasizes the transformative potential of personalized medicine in combating COVID-19 and other viral infections.

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Abstract

This study explores the use of AI and machine learning (ML) to stratify COVID-19 patients based on omics data and clinical biomarkers. The researchers developed ML models that predict COVID-19 severity and survival using clinical features, achieving high accuracy. Model explainability analysis identified key biomarkers associated with severe cases and reduced survival, which were further validated by external studies. Weighted gene co-expression network analysis provided insights into gene networks associated with COVID-19 severity and clinical features. The findings highlight the potential of AI/ML for personalized medicine in COVID-19 and other viral infections.

Publisher

International Journal of Molecular Sciences

Published On

Mar 26, 2023

Authors

Babatunde Bello, Yogesh N Bundey, Roshan Bhave, Maksim Khotimchenko, Szczepan W Baran, Kaushik Chakravarty, Jyotika Varshney

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