PreprintArticleVersion 1Preserved in Portico This version is not peer-reviewed
Integrating AI/ML Models for Patient Stratification Leveraging Omics Dataset and Clinical Biomarkers from COVID-19 Patients: A Promising Approach to Personalized Medicine
Version 1
: Received: 28 February 2023 / Approved: 1 March 2023 / Online: 1 March 2023 (03:37:48 CET)
How to cite:
Bello, B.; Bundey, Y.N.; Bhave, R.; Khotimchenko, M.; Baran, S.W.; Chakravarty, K.; Varshney, J. Integrating AI/ML Models for Patient Stratification Leveraging Omics Dataset and Clinical Biomarkers from COVID-19 Patients: A Promising Approach to Personalized Medicine. Preprints2023, 2023030009. https://doi.org/10.20944/preprints202303.0009.v1.
Bello, B.; Bundey, Y.N.; Bhave, R.; Khotimchenko, M.; Baran, S.W.; Chakravarty, K.; Varshney, J. Integrating AI/ML Models for Patient Stratification Leveraging Omics Dataset and Clinical Biomarkers from COVID-19 Patients: A Promising Approach to Personalized Medicine. Preprints 2023, 2023030009. https://doi.org/10.20944/preprints202303.0009.v1.
Cite as:
Bello, B.; Bundey, Y.N.; Bhave, R.; Khotimchenko, M.; Baran, S.W.; Chakravarty, K.; Varshney, J. Integrating AI/ML Models for Patient Stratification Leveraging Omics Dataset and Clinical Biomarkers from COVID-19 Patients: A Promising Approach to Personalized Medicine. Preprints2023, 2023030009. https://doi.org/10.20944/preprints202303.0009.v1.
Bello, B.; Bundey, Y.N.; Bhave, R.; Khotimchenko, M.; Baran, S.W.; Chakravarty, K.; Varshney, J. Integrating AI/ML Models for Patient Stratification Leveraging Omics Dataset and Clinical Biomarkers from COVID-19 Patients: A Promising Approach to Personalized Medicine. Preprints 2023, 2023030009. https://doi.org/10.20944/preprints202303.0009.v1.
Abstract
The COVID-19 pandemic has presented an unprecedented challenge to the healthcare system. Identifying the genomics and clinical biomarkers for effective patient stratification and management is critical to controlling the spread of the disease. Omics datasets provide a wealth of information that can aid in understanding the underlying molecular mechanisms of COVID-19 and identifying potential biomarkers for patient stratification. Artificial intelligence (AI) and machine learning (ML) algorithms have been increasingly used to analyze large-scale omics and clinical datasets for patient stratification. In this manuscript, we demonstrate the recent advances and predictive accuracies in AI and ML-based patient stratification modeling linking omics and clinical biomarker datasets, focusing on COVID-19 patients. Our ML model not only demonstrates that clinical features are enough an indicator of COVID-19 severity and survival but also infers what clinical features are more impactful, which makes our approach a useful guide for clinicians for prioritization of best-fit therapeutics for a given cohort of patients. Moreover, with weighted gene network analysis, we are able to provide insights into gene networks that have a significant association with COVID-19 severity and clinical features. Finally, we have demonstrated the importance of clinical biomarkers in identifying high-risk patients and predicting disease progression.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
,
,
*
