Active Inference for Antimicrobial Resistance Mechanism Identification: Adaptive, Self-Learning Diagnostic Test Selection Using the Prometheus 1T Engine

Key Points

• The study aims to develop an advanced system for identifying mechanisms of antimicrobial resistance using active inference techniques.
• Developed the Prometheus 1T Engine using active inference principles.
• Implemented exact Bayesian inference for diagnostic test selection.
• Utilized Expected Information Gain to optimize diagnostic processes.
• Validated the approach mathematically against the pymdp library.
• Achieved efficient selection of diagnostic tests for antimicrobial resistance.
• Showed improved accuracy in identifying resistance mechanisms through active inference.
• Demonstrated the system's capability of self-learning in diagnostic test selection.