Multi-Agent Approach for Sepsis Management
10.4258/hir.2025.31.2.209
- Author:
Victor IAPASCURTA
1
;
Ion FIODOROV
;
Adrian BELII
;
Viorel BOSTAN
Author Information
1. Department of Software Engineering and Automatics, Technical University of Moldova, Chisinnu, Republic of Moldova
- Publication Type:Case Report
- From:Healthcare Informatics Research
2025;31(2):209-214
- CountryRepublic of Korea
- Language:English
-
Abstract:
Objectives:The high incidence of sepsis necessitates the development of practical decision-making tools for intensivists, especially during the early, critical phases of management. This study evaluates a multi-agent system intended to assist clinicians with antibiotic therapy and adherence to current sepsis management guidelines before diagnostic results become available.
Methods:A multi-agent system incorporating three specialized agents was developed: a sepsis management agent, an antibiotic recommendation agent, and a sepsis guidelines compliance agent. A sepsis case from the MIMIC IV database, organized as a clinical vignette, was used to integrate and test these agents for generating management recommendations. The system leverages retrieval-augmented generation to improve decision-making through the integration of current literature and guidelines.
Results:The application produced management recommendations for a sepsis case associated with pneumonia, including early initiation of broad-spectrum antibiotics and close monitoring for clinical deterioration. Two expert intensivists evaluated these recommendations as “acceptable” and reported moderate interrater agreement (Cohen’s kappa = 0.622, p = 0.003) across various aspects of recommendation usefulness.
Conclusions:The multi-agent system shows promise in enhancing decision-making for sepsis management by optimizing antibiotic therapy and ensuring guideline compliance. However, reliance on a single case study limits the generalizability of the findings, highlighting the need for broader validation in diverse clinical settings to improve patient outcomes.
