Imperial College London
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DrDavidAntcliffe

Faculty of Medicine, Department of Surgery & Cancer

Clinical Senior Lecturer in Critical Medicine
 
 
 
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Contact

 

d.antcliffe

 
 
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Location

 

Intensive Care UnitCharing Cross HospitalCharing Cross Campus

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Summary

 

Publications

Citation

BibTex format

@article{Komorowski:2022:10.1016/j.ebiom.2022.104394,
author = {Komorowski, M and Green, A and Tatham, KC and Seymour, C and Antcliffe, D},
doi = {10.1016/j.ebiom.2022.104394},
journal = {EBioMedicine},
pages = {1--10},
title = {Sepsis biomarkers and diagnostic tools with a focus on machine learning.},
url = {http://dx.doi.org/10.1016/j.ebiom.2022.104394},
volume = {86},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Over the last years, there have been advances in the use of data-driven techniques to improve the definition, early recognition, subtypes characterisation, prognostication and treatment personalisation of sepsis. Some of those involve the discovery or evaluation of biomarkers or digital signatures of sepsis or sepsis sub-phenotypes. It is hoped that their identification may improve timeliness and accuracy of diagnosis, suggest physiological pathways and therapeutic targets, inform targeted recruitment into clinical trials, and optimise clinical management. Given the complexities of the sepsis response, panels of biomarkers or models combining biomarkers and clinical data are necessary, as well as specific data analysis methods, which broadly fall under the scope of machine learning. This narrative review gives a brief overview of the main machine learning techniques (mainly in the realms of supervised and unsupervised methods) and published applications that have been used to create sepsis diagnostic tools and identify biomarkers.
AU  - Komorowski,M
AU  - Green,A
AU  - Tatham,KC
AU  - Seymour,C
AU  - Antcliffe,D
DO  - 10.1016/j.ebiom.2022.104394
EP  - 10
PY  - 2022///
SN  - 2352-3964
SP  - 1
TI  - Sepsis biomarkers and diagnostic tools with a focus on machine learning.
T2  - EBioMedicine
UR  - http://dx.doi.org/10.1016/j.ebiom.2022.104394
UR  - https://www.ncbi.nlm.nih.gov/pubmed/36470834
UR  - https://www.sciencedirect.com/science/article/pii/S235239642200576X?via%3Dihub
UR  - http://hdl.handle.net/10044/1/101259
VL  - 86
ER  -
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