Detecting Antimicrobial Resistance Through MALDI-TOF Mass Spectrometry with Statistical Guarantees Using Conformal Prediction

Nina Corvelo Benz, Lucas Miranda, Dexiong Chen, Janko Sattler, Karsten M. Borgwardt

Abstract: Antimicrobial resistance is a global health challenge, complicating the treatment of bacterial infections and leading to higher patient morbidity and mortality. Rapid and reliable identification of resistant pathogens is crucial for timely and effective therapeutic interventions, but traditional culture-based methods are time-consuming. Machine learning models using bacterial MALDI-TOF mass spectrometry data have shown promising results in early resistance prediction—with predictions available at least 24 h earlier than conventional phenotypical test results. However, their clinical adoption has been hindered by subpar predictive performance and the lack of interpretable, statistically valid uncertainty estimates. In this work, we introduce a novel antimicrobial resistance prediction framework that addresses this gap with a novel knowledge-graph-enhanced conformal predictor. Conformal prediction (CP) constructs prediction sets with statistical coverage guarantees, ensuring that bacterial resistance to a certain antibiotic is detected with a specified error rate. Our proposed conformal predictor constructs improved prediction sets over standard CP approaches using a knowledge graph capturing the interdependencies in antimicrobial resistance patterns. In addition, we introduce a novel classifier framework that overcomes the limitations of previous efforts by incorporating multigraph-based antibiotic representations. Evaluating our approach on Klebsiella pneumoniae, we demonstrate state-of-the-art predictive performance across most clinically highly-relevant antibiotics. Furthermore, we show that our knowledge-graph-enhanced conformal predictor reduces false discovery rates while maintaining reliable coverage guarantees compared to standard CP approaches. All code required to reproduce the presented results can be found under https://github.com/BorgwardtLab/ConformalAMR.