ADVANCING THE UNDERSTANDING OF CLINICAL SEPSIS USING GENE EXPRESSION–DRIVEN MACHINE LEARNING TO IMPROVE PATIENT OUTCOMES Article Swipe
Asrar Rashid
,
Feras Al‐Obeidat
,
Wael Hafez
,
Govind Benakatti
,
Rayaz A. Malik
,
Christos A. Koutentis
,
Javed Sharief
,
Joe Brierley
,
Nasir A. Quraishi
,
Zainab Malik
,
Arif Reza Anwary
,
Hoda Alkhzaimi
,
Syed Ahmed Zaki
,
Praveen Khilnani
,
Raziya Kadwa
,
Rajesh Phatak
,
Maike Schumacher
,
M Guftar Shaikh
,
Ahmed Al‐Dubai
,
Amir Hussain
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1097/shk.0000000000002227
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1097/shk.0000000000002227
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.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1097/shk.0000000000002227
- OA Status
- hybrid
- Cited By
- 6
- References
- 53
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4387077521
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4387077521Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1097/shk.0000000000002227Digital Object Identifier
- Title
-
ADVANCING THE UNDERSTANDING OF CLINICAL SEPSIS USING GENE EXPRESSION–DRIVEN MACHINE LEARNING TO IMPROVE PATIENT OUTCOMESWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-09-22Full publication date if available
- Authors
-
Asrar Rashid, Feras Al‐Obeidat, Wael Hafez, Govind Benakatti, Rayaz A. Malik, Christos A. Koutentis, Javed Sharief, Joe Brierley, Nasir A. Quraishi, Zainab Malik, Arif Reza Anwary, Hoda Alkhzaimi, Syed Ahmed Zaki, Praveen Khilnani, Raziya Kadwa, Rajesh Phatak, Maike Schumacher, M Guftar Shaikh, Ahmed Al‐Dubai, Amir HussainList of authors in order
- Landing page
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https://doi.org/10.1097/shk.0000000000002227Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1097/shk.0000000000002227Direct OA link when available
- Concepts
-
Sepsis, Gene, Expression (computer science), Gene expression, Computational biology, Intensive care medicine, Medicine, Bioinformatics, Computer science, Biology, Internal medicine, Genetics, Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
6Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 4Per-year citation counts (last 5 years)
- References (count)
-
53Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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