Speaker
Description
Introduction
Rapid results from antimicrobial susceptibility testing (AST) are essential to guide the antimicrobial therapy of critically ill patients. Recent developments have revealed that readily available matrix-assisted laser desorption-ionization-time of flight (MALDI-TOF) mass spectrometry data, which is routinely used for bacterial species identification, can also be used to predict antimicrobial resistance with machine learning (ML) techniques (1,2). The required preprocessing of mass spectral data typically follows the seminal work of Weis et al. (1), but numerous possible variants exist and their potential impact on the predictive performance of ML models remains to be studied.
Objectives
We systematically study various preprocessing strategies for mass spectral data with respect to
a) their influence on the performance of different ML algorithms predicting antimicrobial resistance (AMR).
b) their impact on model transferability to external data sets.
The methods under study include different binning strategies (such as linear, exponential and dynamic binning) and parameters as well as peak alignment with respect to different reference sets.
Materials & Methods
We benchmark different ML pipelines using nested cross-validation on approximately 10.000 E. Coli MALDI-TOF spectra from clinical routine at the University Hospital Münster. The pipelines include various preprocessing strategies and different learners, such as eXtreme Gradient Boosting, Random Forest, and Elastic Nets, as well as hyperparameter tuning. We evaluate and compare the performance of the resulting models for prediction of cefotaxime resistance in E. Coli both on local and on publicly available external data.
Results
We present the results of our extensive benchmark experiment and make suggestions for the preprocessing of mass spectral data. We discuss the impact on predictive performance and generalizability of ML models.
Summary
Data preprocessing is an important step in developing ML models for AMR prediction from mass spectral data which requires specific methods. Our study helps to improve and robustify such models by identifying optimal preprocessing strategies.
References:
[1] Weis, C., Cuénod, A., Rieck, B. et al. Direct antimicrobial resistance prediction from clinical MALDI-TOF mass spectra using machine learning. Nat Med 28, 164–174 (2022).
[2] Wiesmann, N., Enders, D., Westendorf, A., Koch, R., and Schaumburg, F., Prediction of Antimicrobial Resistance from MALDI-TOF Mass Spectra Using Machine Learning: A Validation Study. Accepted for publication in J. Clin. Microbiol.
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