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

Qian Fu, Yuzhe Zhang, Yanfeng Shu, Ming Ding, Lina Yao, Chen Wang

TL;DR

This paper surveys data-driven approaches to antimicrobial resistance (AMR), emphasizing the transmission across human, animal, and environmental domains and the data challenges that accompany it. It synthesizes four key analytics tasks—AMR prediction, antimicrobial stewardship, driver analysis, and novel antimicrobial discovery—and maps them to diverse data sources and analytic methods, from ML and DL to knowledge graphs and Bayesian networks. A core contribution is a comprehensive view of data collection, quality, and bias issues, alongside practical mitigation strategies (denoising, debiasing, causality, and interpretability) and privacy considerations. The work highlights the need for standardized surveillance (e.g., GLASS), cross-domain data integration, and interdisciplinary collaboration to translate data-driven insights into effective AMR interventions and new therapies.

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.

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

TL;DR

This paper surveys data-driven approaches to antimicrobial resistance (AMR), emphasizing the transmission across human, animal, and environmental domains and the data challenges that accompany it. It synthesizes four key analytics tasks—AMR prediction, antimicrobial stewardship, driver analysis, and novel antimicrobial discovery—and maps them to diverse data sources and analytic methods, from ML and DL to knowledge graphs and Bayesian networks. A core contribution is a comprehensive view of data collection, quality, and bias issues, alongside practical mitigation strategies (denoising, debiasing, causality, and interpretability) and privacy considerations. The work highlights the need for standardized surveillance (e.g., GLASS), cross-domain data integration, and interdisciplinary collaboration to translate data-driven insights into effective AMR interventions and new therapies.

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.

Paper Structure

This paper contains 20 sections, 3 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Data Analytics Tasks in AMR Research
  3. AMR Prediction
  4. Antimicrobial Stewardship
  5. AMR Driver Quantification
  6. Novel Antimicrobial Discovery
  7. AMR Data Sources, Collection, and Challenges
  8. AMR Data Collection and Surveillance
  9. Challenges with Data Collection and Use
  10. Mitigating Data Challenges in ML-based AMR Research
  11. Mitigation Process in Data Collection and Preparation
  12. Mitigation Strategies in Modeling
  13. Data Bias and Fair Modeling
  14. Improving Fairness in Other Medical Domains
  15. Causality for Data Noise and Bias Handling
  16. ...and 5 more sections

Figures (3)

  • Figure 1: AMR transmissions among human, animal and environment: current observations and intervention gaps. Cylinders represent data supporting the AMR transmission pathways. Data analytics tasks are introduced for AMR intervention strategy development.
  • Figure 2: Process phases and potential data challenges of learning-based AMR tasks, where ① stands for data noise and ②-⑨ are 8 different types of data biases.
  • Figure 3: Addressing data issues, mainly noise and bias, spans multiple phases of the machine, notably during the data handling and modelling phases