• Asrar Rashid, Feras Al-Obeida, Wael Hafez, Govind Benakatti, Rayaz A Malik, Christos Koutentis, Javed Sharief, Joe Brierley, Nasir Quraishi, Zainab A Malik, Arif Anwary, Hoda Alkhzaimi, Syed Ahmed Zaki, Praveen Khilnani, Raziya Kadwa, Rajesh Phatak, Maike Schumacher, Guftar Shaikh, Ahmed Al-Dubai, and Amir Hussain.
• The National Research Centre, Egypt.
• Shock. 2024 Jan 1; 61 (1): 4184-18.

AbstractSepsis remains a major challenge that necessitates improved approaches to enhance patient outcomes. This study explored the potential of machine learning (ML) techniques to bridge the gap between clinical data and gene expression information to better predict and understand sepsis. We discuss the application of ML algorithms, including neural networks, deep learning, and ensemble methods, to address key evidence gaps and overcome the challenges in sepsis research. The lack of a clear definition of sepsis is highlighted as a major hurdle, but ML models offer a workaround by focusing on endpoint prediction. We emphasize the significance of gene transcript information and its use in ML models to provide insights into sepsis pathophysiology and biomarker identification. Temporal analysis and integration of gene expression data further enhance the accuracy and predictive capabilities of ML models for sepsis. Although challenges such as interpretability and bias exist, ML research offers exciting prospects for addressing critical clinical problems, improving sepsis management, and advancing precision medicine approaches. Collaborative efforts between clinicians and data scientists are essential for the successful implementation and translation of ML models into clinical practice. Machine learning has the potential to revolutionize our understanding of sepsis and significantly improve patient outcomes. Further research and collaboration between clinicians and data scientists are needed to fully understand the potential of ML in sepsis management.Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the Shock Society.

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