From Data to Action: Charting A Data-Driven Path to Combat Antimicrobial Resistance
It tackles the problem of AMR in healthcare by synthesizing existing research, but it is incremental as it reviews and discusses rather than introduces new findings.
This paper reviews the application of data analytics and machine learning to combat antimicrobial resistance (AMR), summarizing state-of-the-art methods in areas like surveillance and drug discovery while addressing challenges such as data noise and bias.
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.