Early Detection of Sepsis With Machine Learning Techniques: A Brief Clinical Perspective

Frontiers in Medicine

Paolo Pelosi¹  Lorenzo Ball¹  Chiara Robba¹  Filippo Del Puente²  Luca Carmisciano²  Alessio Signori²  Mauro Giacomini³  Sara Mora³  Antonio Vena⁴  Federica Briano⁵  Matteo Bassetti⁵  Daniele Roberto Giacobbe⁵ 

[1] Anaesthesia and Intensive Care, San Martino Policlinico Hospital – IRCCS for Oncology and Neurosciences, Genoa, Italy;Department of Surgical Sciences and Integrated Diagnostics (DISC), University of Genoa, Genoa, Italy;Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy;Department of Informatics Bioengineering, Robotics, and Systems Engineering (DIBRIS), University of Genoa, Genoa, Italy;Infectious Diseases Unit, San Martino Policlinico Hospital – IRCCS for Oncology and Neurosciences, Genoa, Italy;Infectious Diseases Unit, San Martino Policlinico Hospital – IRCCS for Oncology and Neurosciences, Genoa, Italy;Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy;

关键词: sepsis;    machine learning;    artificial intelligence;    early diagnosis;    supervised learning;    unsupervised learning;   

DOI  :  10.3389/fmed.2021.617486

来源: Frontiers

【 摘 要 】

Sepsis is a major cause of death worldwide. Over the past years, prediction of clinically relevant events through machine learning models has gained particular attention. In the present perspective, we provide a brief, clinician-oriented vision on the following relevant aspects concerning the use of machine learning predictive models for the early detection of sepsis in the daily practice: (i) the controversy of sepsis definition and its influence on the development of prediction models; (ii) the choice and availability of input features; (iii) the measure of the model performance, the output, and their usefulness in the clinical practice. The increasing involvement of artificial intelligence and machine learning in health care cannot be disregarded, despite important pitfalls that should be always carefully taken into consideration. In the long run, a rigorous multidisciplinary approach to enrich our understanding in the application of machine learning techniques for the early recognition of sepsis may show potential to augment medical decision-making when facing this heterogeneous and complex syndrome.

【 授权许可】

CC BY