Conformal Prediction with Knowledge Graphs for Reliable Antimicrobial Resistance Detection with MALDI-TOF Mass Spectra

Bacterial antimicrobial resistance is one of the most pressing global health challenges. Infections with resistant pathogens increase patient morbidity and mortality due to limited treatment options. Rapid and reliable identification of resistance is therefore crucial. However, conventional culture-based diagnostics are slow, typically requiring at least 48 hours from patient sample arrival to result. In contrast, matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry, routinely used for species identification, can provide data 24 hours earlier. Repurposing this technique for antimicrobial resistance prediction has shown promise, but limited predictive performance and a lack of statistically grounded uncertainty estimates have hindered clinical integration. To address these issues, we propose an antimicrobial resistance detection framework using a knowledge-graph-enhanced conformal predictor. Conformal prediction outputs sets of likely effective antibiotics with statistical guarantees, ensuring that resistance detection meets a predefined error rate. Our approach improves upon standard conformal prediction by integrating domain knowledge through a drug- and species-specific knowledge graph that captures interdependencies between antibiotics, such as inferable resistance patterns between broad- and narrow-spectrum agents, as well as co-resistance patterns within antibiotic classes. This predictor is layered on top of a novel classifier that surpasses state-of-the-art models and overcomes key technical limitations of earlier approaches. We evaluated our framework on two clinically relevant species, <i>Klebsiella pneumoniae</i> and <i>Escherichia coli</i>, using the DRIAMS dataset. Our results demonstrate that our conformal predictor consistently achieved the expected coverage guarantees and that the knowledge-graph enhancement significantly reduced false discovery rates compared to standard conformal approaches. By adding statistically grounded uncertainty estimates and improving predictive performance, our framework strengthens the reliability of early antimicrobial resistance predictions from MALDI-TOF data. This could support the clinical integration of such rapid diagnostics by increasing trust in their results and enabling better-informed early treatment decisions.1.