From Data to Action: Charting A Data-Driven Path to Combat Antimicrobial Resistance

TL;DR

This paper reviews data-driven and machine learning approaches to understanding, predicting, and combating antimicrobial resistance, emphasizing data challenges, standardization, and interdisciplinary collaboration for future progress.

Contribution

It provides a comprehensive overview of current data analytics and machine learning techniques in AMR research, highlighting challenges and future directions.

Findings

Survey of data sources and methods for AMR analysis

Discussion of denoising and debiasing techniques

Identification of key challenges in data standardization and interoperability

Abstract

Antimicrobial-resistant (AMR) microbes are a growing challenge in healthcare, rendering modern medicines ineffective. AMR arises from antibiotic production and bacterial evolution, but quantifying its transmission remains difficult. With increasing AMR-related data, data-driven methods offer promising insights into its causes and treatments. This paper reviews AMR research from a data analytics and machine learning perspective, summarizing the state-of-the-art and exploring key areas such as surveillance, prediction, drug discovery, stewardship, and driver analysis. It discusses data sources, methods, and challenges, emphasizing standardization and interoperability. Additionally, it surveys statistical and machine learning techniques for AMR analysis, addressing issues like data noise and bias. Strategies for denoising and debiasing are highlighted to enhance fairness and robustness in AMR research. The paper underscores the importance of interdisciplinary collaboration and awareness of data challenges in advancing AMR research, pointing to future directions for innovation and improved methodologies.