Identification of antibiotic collateral sensitivity and resistance interactions in population surveillance data.

Laura B ZwepYob HaakmanKevin L W DuistersJacqueline J MeulmanApostolos Liakopoulos Johan G C van Hasselt

Published in: JAC-antimicrobial resistance (2021)

Our proposed approach is of relevance as a tool for analysis of large-scale population surveillance studies to provide broad systematic identification of collateral effects related to antibiotic resistance, and is made available to the community as an R package. This method can help mapping CS and CR, which could guide combination therapy and prescribing in the future.

Keyphrases

combination therapy
public health
primary care
healthcare
mental health
bioinformatics analysis
high resolution
electronic health record
emergency department
machine learning
high density
deep learning
case control
data analysis
drug induced