Early detection of sepsis using artificial intelligence: a scoping review protocol.

Area of Science:

• Medical Informatics
• Artificial Intelligence in Healthcare
• Clinical Decision Support Systems

Background:

• Sepsis is a life-threatening organ dysfunction due to a dysregulated host response to infection.
• High sepsis case fatality rates necessitate improved early detection methods.
• Artificial intelligence (AI) offers potential for developing accurate sepsis risk prediction systems.

Purpose of the Study:

• To conduct a scoping review of existing AI methods for early sepsis detection.
• To summarize the current literature on AI-driven sepsis detection strategies.
• To identify research gaps and inform future innovations in AI for sepsis management.

Main Methods:

• Scoping review utilizing the Arksey and O'Malley framework.
• Comprehensive literature search across major databases (PubMed, Scopus, Web of Science, etc.) and ClinicalTrials.gov.
• Inclusion of studies published in English between 1990 and 2020, encompassing machine learning and mathematical models.

Main Results:

• The review will provide a descriptive and thematic analysis of AI applications in early sepsis detection.
• Knowledge on the current state of research and innovation will be conveyed.
• Identified AI methods will be summarized to inform future development.

Conclusions:

• AI holds significant promise for enhancing the accuracy and timeliness of sepsis detection.
• Further research and development of AI-based decision support systems are crucial.
• Dissemination of findings will aid in advancing digital health solutions for sepsis.