From Data to Action: Charting A Data-Driven Path to Combat Antimicrobial Resistance
Journal:
arXiv
Published Date:
Jan 30, 2025
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.
