Machine learning models for Neisseria gonorrhoeae antimicrobial susceptibility tests

Abstract

Neisseria gonorrhoeae is an urgent public health threat due to the emergence of antibiotic resistance. As most isolates in the United States are susceptible to at least one antibiotic, rapid molecular antimicrobial susceptibility tests (ASTs) would offer the opportunity to tailor antibiotic therapy, thereby expanding treatment options. With genome sequence and antibiotic resistance phenotype data for nearly 20,000 clinical N. gonorrhoeae isolates now available, there is an opportunity to use statistical methods to develop sequence-based diagnostics that predict antibiotic susceptibility from genotype. N. gonorrhoeae, therefore, provides a useful example illustrating how to apply machine learning models to aid in the design of sequence-based ASTs. We present an overview of this framework, which begins with establishing the assay technology, the performance criteria, the population in which the diagnostic will be used, and the clinical goals, and extends to the choices that must be made to arrive at a set of features with the desired properties for predicting susceptibility phenotype from genotype. While we focus on the example of N. gonorrhoeae, the framework generalizes to other organisms for which large-scale genotype and antibiotic resistance data can be combined to aid in diagnostics development.

Keywords: Neisseria gonorrhoeae; antimicrobial resistance; diagnostics; machine learning.

Figure 1.

Framework for designing machine learning models for antimicrobial susceptibility diagnostics. Rounded boxes represent sections in the text. Black lines represent the order of decisions. Rectangular boxes to the right of each rounded box represent the key design decisions at each step. The rightmost column contains the considerations for each choice. Colors are coded to align with subsections of the first section listed at the top of the figure, with grey bullets representing computational and machine learning considerations.