Sepsis biomarkers and diagnostic tools with a focus on machine learning

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.

Keywords: Biomarkers; Clustering; Machine learning; Phenotypes; Precision medicine; Sepsis.

Fig. 1

Objectives and principles of supervised and unsupervised learning. Supervised learning methods link input data and labels, and are typically used in sepsis prediction algorithms. Unsupervised learning has been applied to highlight the underlying structure or to unearth hidden patterns in high-dimensional datasets. HTE: Heterogeneity of Treatment Effect.