用数据驱动方法破解耐药菌传播难题，助力精准防控。
From Data to Action: Charting A Data-Driven Path to Combat Antimicrobial Resistance
- 从监测到药物研发，系统梳理机器学习在耐药研究中的应用路径。
- 强调数据噪声与偏倚问题，提出去噪去偏策略提升模型可靠性。
- 适合医学信息学、公共卫生及人工智能交叉研究者参考。
耐药微生物（AMR）正日益威胁现代医疗，导致药物失效。虽然抗生素使用与细菌进化是主要诱因，但其传播机制难以量化。随着耐药相关数据的积累，数据驱动方法为揭示病因和治疗方案提供了新视角。本文从数据分析与机器学习角度综述了耐药研究进展，涵盖监测、预测、药物发现、管理策略与驱动因素分析等关键领域。讨论了数据来源、分析方法与挑战，强调标准化与互操作性的重要性。系统回顾了统计与机器学习技术在耐药分析中的应用，针对数据噪声与偏倚问题提出应对策略。文章呼吁跨学科协作，重视数据局限性，指明未来创新方向与方法改进路径。
原文摘要 · Abstract (English)
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.
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