FLAP:用于将自由文本地址链接到军火调查唯一属性参考号数据库的框架
Huayu Zhang1, Arlene Casey1, Imane Guellil1
1Advanced Care Research Centre, The Usher Institute, College of Medicine and Veterinary Medicine, University of Edinburgh, Edinburgh, United Kingdom.
Frontiers in digital health
|December 13, 2023
概括
一个新的开源工具,FLAP,准确地将英国自由文本地址链接到唯一财产参考号码数据库. 这种机器学习框架改善了数字卫生系统和资源分配的地理空间分析.
科学领域:
- 地理空间分析是什么?
- 数字健康数字健康
- 医疗信息学 医疗信息学
背景情况:
- 将自由文本地址与独特的财产标识符联系起来,对于数字健康领域的地理空间分析至关重要.
- 现有的工具缺乏开源可用性和对此任务的验证性能.
- 准确地址链接支持诸如识别养老院居民和优化资源分配等应用程序.
研究的目的:
- 开发和验证一个可通用,开源的框架,将自由文本的英国地址与军火调查唯一财产参考号码 (UPRN) 数据库联系起来.
- 为了提高数字健康应用的地址匹配的准确性和可用性.
- 提供基于机器学习的解决方案,可适应新数据和持续改进.
主要方法:
- 开发了FLAP (链接自由文本地址与军火调查UPRN数据库的框架),这是一个基于Python的开源工具.
- 采用了机器学习匹配分类器,并结合了模糊对齐算法来生成特征.
- 通过将NHS Tayside和NHS Fife的社区健康指数 (CHI) 自由文本地址与UPRN数据库连接来测试FLAP.
主要成果:
- 在真实世界测试数据集上,实现了0.992的调整匹配精度.
- 证明了对打字错误,替代格式和不完整地址信息等变化的稳定性.
- 通过可定制匹配值和顶级候选记录的选择,提高了可用性.
结论:
- FLAP提供了一个高性能和用户友好的解决方案,用于将自由文本英国地址链接到UPRN数据库.
- 该框架的机器学习方法确保了适应性和持续改进的潜力.
- FLAP为数字健康和资源管理提供了必要的地理空间分析.
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