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Advancing the Understanding of Clinical Sepsis Using Gene Expression-Driven Machine Learning to Improve Patient Outcomes

Rashid, Asrar; Al-Obeida, Feras; Hafez, Wael; Benakatti, Govind; Malik, Rayaz A; Koutentis, Christos; Sharief, Javed; Brierley, Joe; Quraishi, Nasir; Malik, Zainab A; Anwary, Arif; Alkhzaimi, Hoda; Zaki, Syed Ahmed; Khilnani, Praveen; Kadwa, Raziya; Phatak, Rajesh; Schumacher, Maike; Shaikh, Guftar; Al-Dubai, Ahmed; Hussain, Amir


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


Sepsis 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. ML 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.

Journal Article Type Review
Online Publication Date Sep 25, 2023
Publication Date 2024-01
Deposit Date Jan 26, 2024
Publicly Available Date Jan 26, 2024
Journal Shock
Print ISSN 1073-2322
Publisher Lippincott, Williams & Wilkins
Peer Reviewed Peer Reviewed
Volume 61
Issue 1
Pages 4-18
Keywords Machine Learning, Sepsis, Gene- Expression, Septic Shock
Public URL


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