AI Article Synopsis
- Advances in data-driven techniques have improved the understanding and treatment of sepsis, particularly in areas like early recognition and personalizing medical care.* -
- Researchers are focusing on discovering biomarkers and digital signatures to enhance the diagnosis and management of sepsis sub-types, which could lead to better clinical outcomes.* -
- The review discusses various machine learning methods, including supervised and unsupervised approaches, that have been applied to develop diagnostic tools and identify important biomarkers for sepsis.*
Article Abstract
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.
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|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9783125 | PMC |
| http://dx.doi.org/10.1016/j.ebiom.2022.104394 | DOI Listing |
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