[Paper Review] From Data to Action: Charting A Data-Driven Path to Combat Antimicrobial Resistance

Qian Fu, Yuzhe Zhang+4 more|ArXiv.org|Jan 30, 2025

Antibiotic Use and Resistance3 citations

TL;DR

A comprehensive survey of data-driven AMR research, covering surveillance, prediction, stewardship, driver analysis, and novel antimicrobial discovery, with discussion of data sources, challenges, and mitigation strategies.

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.

Motivation & Objective

• Summarize state-of-the-art data analytics and ML methods applied to AMR tasks.
• Highlight data sources, collection practices, and interoperability challenges in AMR research.
• Discuss data challenges (noise, bias, privacy) and strategies to improve robustness and fairness.
• Identify interdisciplinary needs and pathways for future robustness, fairness, and innovation in AMR data science.

Proposed method

• Review AMR tasks (prediction, stewardship, driver analysis, novel discovery) and their data needs.
• Map data sources to AMR tasks and summarize public data resources (e.g., ARG databases, EHRs, GLASS, ResistanceMap).
• Discuss data handling challenges (noise, biases, privacy) and mitigation strategies (denoising, debiasing).
• Describe data collection frameworks and One Health surveillance practices.
• Compare analytical methods from statistics to deep learning across AMR tasks.

Experimental results

Research questions

• RQ1 What are the key AMR data analytics tasks and how are ML methods applied to each?
• RQ2 What data sources and standardization practices support AMR surveillance, prediction, stewardship, driver analysis, and discovery?
• RQ3 What data challenges (noise, bias, privacy) hinder AMR data science, and what strategies mitigate their impact?
• RQ4 How can interoperability and interdisciplinary collaboration improve robustness and fairness in AMR research?

Key findings

• AMR research comprises prediction, stewardship, driver analysis, and novel antimicrobial discovery, each using distinct data types and ML approaches.
• Public data sources and databases (e.g., ARG databases, MALDI-TOF, EHRs, GLASS, ResistanceMap) underpin major AMR tasks and enable data-driven insights.
• Data challenges such as noise, bias, and privacy concerns are pervasive, with proposed mitigations including denoising, debiasing, and privacy-preserving techniques.
• The paper advocates for One Health data integration and interdisciplinary collaboration to address complex AMR dynamics and improve robustness and fairness in ML applications.