Assessing putative bias in prediction of anti-microbial resistance from real-world genotyping data under explicit causal assumptions

作者：

Highlights：

• Whole genome sequencing coupled with computational methods are increasingly used for predict antimicrobial resistance (AMR)

• AMR databases and collections present sampling bias (geographical, temporal, and species-specific)

• Causally-informed methods can aid the development of AMR prediction tools rendering them more robust with respect to bias

摘要

•Whole genome sequencing coupled with computational methods are increasingly used for predict antimicrobial resistance (AMR)•AMR databases and collections present sampling bias (geographical, temporal, and species-specific)•Causally-informed methods can aid the development of AMR prediction tools rendering them more robust with respect to bias

论文关键词：Antimicrobial resistance,Biomedical informatics,Causal methods,Directed acyclic graph,Epidemiology,Explainability,Interpretability,Propensity score

论文评审过程：Received 25 October 2021, Revised 11 May 2022, Accepted 23 May 2022, Available online 3 June 2022, Version of Record 6 June 2022.

论文官网地址：https://doi.org/10.1016/j.artmed.2022.102326