Abstract
Sepsis is one of the main causes of death in critically ill patients. Despite the continuous development of medical technology in recent years, its morbidity and mortality are still high. This is mainly related to the delay in starting treatment and non-adherence of clinical guidelines. Artificial intelligence (AI) is an evolving field in medicine, which has been used to develop a variety of innovative Clinical Decision Support Systems. It has shown great potential in predicting the clinical condition of patients and assisting in clinical decision-making. AI-derived algorithms can be applied to multiple stages of sepsis, such as early prediction, prognosis assessment, mortality prediction, and optimal management. This review describes the latest literature on AI for clinical decision support in sepsis, and outlines the application of AI in the prediction, diagnosis, subphenotyping, prognosis assessment, and clinical management of sepsis. In addition, we discussed the challenges of implementing and accepting this non-traditional methodology for clinical purposes.
Highlights
Sepsis is a syndrome in which infection causes host response imbalance
A study reported a sepsis prediction model based on ensemble learning framework, which combines artificial features extracted from advanced clinical knowledge and deep features based on automatic extraction of long-term and short-term memory (LSTM) neural networks [20]
A deep neural network (DNN) model developed using LSTM can evaluate the clinical status of patients after treatment in the intensive care unit (ICU), thereby predicting the mortality rate within 96 h after admission
Summary
Reviewed by: Yongan Xu, Zhejiang University, China Sandeep Reddy, Deakin University, Australia Jiao Liu, Shanghai Jiao Tong University, China. Specialty section: This article was submitted to Intensive Care Medicine and Anesthesiology, a section of the journal
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