Rwanda欲用大数据分析提升糖尿病管理，提出可解释机器学习框架
Implementation of Big Data Analytics for Diabetes Management: Needs Assessment in the Rwanda Healthcare System
- 通过5天工作坊调研25名关键人员，评估系统准备度
- 发现数据整合与技术能力是主要障碍，但已有电子病历基础
- 提出可解释模型框架，适合政策制定者与医疗技术人员参考
糖尿病是一种慢性代谢疾病，若未及早诊断和管理，可能导致严重健康问题。大数据分析（BDA）与机器学习为处理大规模健康数据、支持早期检测和优化治疗决策提供了有效工具。然而，这些技术在临床实践中的应用仍有限。本研究评估了卢旺达医疗体系在糖尿病管理中实施大数据分析的准备情况。随着该国持续推广电子病历与卫生信息系统，预测、监测与临床决策的改进机会逐渐显现。通过为期五天的工作坊，对25名关键利益相关者（包括临床医生、数据管理者、政策制定者、医学研究人员、营养师和技术提供方）进行了调研，识别出现有差距。研究结果揭示了实施潜力与主要挑战，并基于此提出一个实用的大数据分析框架，采用可解释机器学习模型以支持糖尿病管理策略。
原文摘要 · Abstract (English)
Diabetes is a chronic metabolic disease that can lead to serious health problems if not diagnosed and managed early. Big Data Analytics (BDA) and machine learning offer practical tools for analyzing large health datasets and supporting early detection and better treatment decisions. However, their use in routine clinical practice is still limited. This study examines the readiness of Rwanda's healthcare system to adopt big data analytics for diabetes management. As the country continues to expand its use of electronic medical records and health information systems, new opportunities arise for improving prediction, monitoring, and clinical decision-making. A five-day workshop involving 25 key stakeholders, including clinicians, data managers, policymakers, medical researchers, nutritionists, and technology providers, was conducted to assess preparedness and identify existing gaps. The findings highlight both the potential and the main challenges of BDA implementation. Based on these results, the paper proposes a practical BDA framework to support diabetes management strategies using explainable machine learning models.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。