摘 要
药物不良反应（ADR）是全球公共卫生领域的重要问题，其监测与管理对保障用药安全具有重要意义。本研究旨在构建系统化的药物不良反应监测与管理策略，以提高ADR的识别效率和管理水平。通过整合国内外相关文献数据，并结合实际案例分析，采用定量与定性相结合的研究方法，探索适用于我国医疗体系的ADR监测体系优化路径。研究发现，当前ADR监测存在信息碎片化、报告主动性不足及数据分析能力有限等问题，而引入人工智能技术可显著提升ADR监测的精准性和时效性。基于此，本研究提出了一种融合大数据分析与机器学习算法的智能化监测框架，能够实现ADR的早期预警与动态追踪。此外，研究还强调了多部门协作机制的重要性，建议通过政策引导和技术支持完善监测网络。该研究的创新点在于将新兴信息技术与传统监测手段有机结合，为药物安全管理提供了新思路，同时为相关政策制定提供了科学依据，具有重要的实践价值和推广意义。
关键词：药物不良反应; 智能化监测框架; 大数据分析; 机器学习; 多部门协作机制
Abstract
Drug adverse reactions (ADRs) represent a significant issue in global public health, and their monitoring and management are crucial for ensuring medication safety. This study aims to construct a systematic strategy for ADR monitoring and management to enhance the efficiency of ADR identification and the level of management. By integrating domestic and international literature data and combining it with case analysis, a mixed-methods approach incorporating both quantitative and qualitative research was adopted to explore optimization pathways for an ADR monitoring system suitable for China’s healthcare system. The findings indicate that current ADR monitoring faces challenges such as information fragmentation, insufficient proactive reporting, and limited data analysis capabilities. The introduction of artificial intelligence technology can significantly improve the accuracy and timeliness of ADR monitoring. Based on these insights, this study proposes an intelligent monitoring fr amework that integrates big data analytics and machine learning algorithms, enabling early warning and dynamic tracking of ADRs. Furthermore, the study emphasizes the importance of multi-departmental collaboration mechanisms and recommends improving the monitoring network through policy guidance and technical support. The innovation of this research lies in the organic combination of emerging information technologies with traditional monitoring methods, providing new approaches for drug safety management and offering scientific evidence for relevant policy formulation. This study holds important practical value and implications for broader application.
Key words:Drug Adverse Reaction; Intelligent Monitoring fr amework; Big Data Analysis; Machine Learning; Multi-Department Collaboration Mechanism
目 录
中文摘要 I
英文摘要 II
引 言 1
第1章、药物不良反应监测体系构建 2
1.1、监测体系的现状分析 2
1.2、关键技术与方法应用 2
1.3、体系建设的挑战与对策 3
第2章、数据驱动的不良反应识别 4
2.1、数据采集与处理方法 4
2.2、模型构建与算法优化 4
2.3、实际案例分析与验证 5
第3章、不良反应风险管理策略 6
3.1、风险评估的核心要素 6
3.2、管理策略的设计原则 6
3.3、实施效果与改进方向 7
第4章、政策支持与公众参与机制 8
4.1、政策框架的完善路径 8
4.2、公众教育与意识提升 8
4.3、多方协作的实现模式 8
结 论 10
参考文献 11
药物不良反应（ADR）是全球公共卫生领域的重要问题，其监测与管理对保障用药安全具有重要意义。本研究旨在构建系统化的药物不良反应监测与管理策略，以提高ADR的识别效率和管理水平。通过整合国内外相关文献数据，并结合实际案例分析，采用定量与定性相结合的研究方法，探索适用于我国医疗体系的ADR监测体系优化路径。研究发现，当前ADR监测存在信息碎片化、报告主动性不足及数据分析能力有限等问题，而引入人工智能技术可显著提升ADR监测的精准性和时效性。基于此，本研究提出了一种融合大数据分析与机器学习算法的智能化监测框架，能够实现ADR的早期预警与动态追踪。此外，研究还强调了多部门协作机制的重要性，建议通过政策引导和技术支持完善监测网络。该研究的创新点在于将新兴信息技术与传统监测手段有机结合，为药物安全管理提供了新思路，同时为相关政策制定提供了科学依据，具有重要的实践价值和推广意义。
关键词：药物不良反应; 智能化监测框架; 大数据分析; 机器学习; 多部门协作机制
Abstract
Drug adverse reactions (ADRs) represent a significant issue in global public health, and their monitoring and management are crucial for ensuring medication safety. This study aims to construct a systematic strategy for ADR monitoring and management to enhance the efficiency of ADR identification and the level of management. By integrating domestic and international literature data and combining it with case analysis, a mixed-methods approach incorporating both quantitative and qualitative research was adopted to explore optimization pathways for an ADR monitoring system suitable for China’s healthcare system. The findings indicate that current ADR monitoring faces challenges such as information fragmentation, insufficient proactive reporting, and limited data analysis capabilities. The introduction of artificial intelligence technology can significantly improve the accuracy and timeliness of ADR monitoring. Based on these insights, this study proposes an intelligent monitoring fr amework that integrates big data analytics and machine learning algorithms, enabling early warning and dynamic tracking of ADRs. Furthermore, the study emphasizes the importance of multi-departmental collaboration mechanisms and recommends improving the monitoring network through policy guidance and technical support. The innovation of this research lies in the organic combination of emerging information technologies with traditional monitoring methods, providing new approaches for drug safety management and offering scientific evidence for relevant policy formulation. This study holds important practical value and implications for broader application.
Key words:Drug Adverse Reaction; Intelligent Monitoring fr amework; Big Data Analysis; Machine Learning; Multi-Department Collaboration Mechanism
目 录
中文摘要 I
英文摘要 II
引 言 1
第1章、药物不良反应监测体系构建 2
1.1、监测体系的现状分析 2
1.2、关键技术与方法应用 2
1.3、体系建设的挑战与对策 3
第2章、数据驱动的不良反应识别 4
2.1、数据采集与处理方法 4
2.2、模型构建与算法优化 4
2.3、实际案例分析与验证 5
第3章、不良反应风险管理策略 6
3.1、风险评估的核心要素 6
3.2、管理策略的设计原则 6
3.3、实施效果与改进方向 7
第4章、政策支持与公众参与机制 8
4.1、政策框架的完善路径 8
4.2、公众教育与意识提升 8
4.3、多方协作的实现模式 8
结 论 10
参考文献 11
