Document Type
Article
Source of Publication
Shock
Publication Date
1-1-2024
Abstract
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. 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.
DOI Link
ISSN
Publisher
Ovid Technologies (Wolters Kluwer Health)
Volume
61
Issue
1
First Page
4
Last Page
18
Disciplines
Computer Sciences
Keywords
gene expression, Machine learning, sepsis, septic shock
Scopus ID
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Recommended Citation
Rashid, Asrar; Al-Obeidat, 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, M. Guftar; Al-Dubai, Ahmed; and Hussain, Amir, "Advancing the Understanding of Clinical Sepsis Using Gene Expression–driven Machine Learning to Improve Patient Outcomes" (2024). All Works. 6373.
https://zuscholars.zu.ac.ae/works/6373
Indexed in Scopus
yes
Open Access
yes
Open Access Type
Hybrid: This publication is openly available in a subscription-based journal/series