Zhang, H;
Casey, A;
Guellil, I;
Suárez-Paniagua, V;
MacRae, C;
Marwick, C;
Wu, H;
... Alex, B; + view all
(2023)
FLAP: a framework for linking free-text addresses to the Ordnance Survey Unique Property Reference Number database.
Frontiers in Digital Health
, 5
, Article 1186208. 10.3389/fdgth.2023.1186208.
Preview |
PDF
fdgth-05-1186208.pdf - Published Version Download (934kB) | Preview |
Abstract
Introduction: Linking free-text addresses to unique identifiers in a structural address database [the Ordnance Survey unique property reference number (UPRN) in the United Kingdom (UK)] is a necessary step for downstream geospatial analysis in many digital health systems, e.g., for identification of care home residents, understanding housing transitions in later life, and informing decision making on geographical health and social care resource distribution. However, there is a lack of open-source tools for this task with performance validated in a test data set. Methods: In this article, we propose a generalisable solution (A Framework for Linking free-text Addresses to Ordnance Survey UPRN database, FLAP) based on a machine learning–based matching classifier coupled with a fuzzy aligning algorithm for feature generation with better performance than existing tools. The framework is implemented in Python as an Open Source tool (available at Link). We tested the framework in a real-world scenario of linking individual’s ((Formula presented.)) addresses recorded as free text in the Community Health Index (CHI) of National Health Service (NHS) Tayside and NHS Fife to the Unique Property Reference Number database (UPRN DB). Results: We achieved an adjusted matching accuracy of 0.992 in a test data set randomly sampled ((Formula presented.)) from NHS Tayside and NHS Fife CHI addresses. FLAP showed robustness against input variations including typographical errors, alternative formats, and partially incorrect information. It has also improved usability compared to existing solutions allowing the use of a customised threshold of matching confidence and selection of top (Formula presented.) candidate records. The use of machine learning also provides better adaptability of the tool to new data and enables continuous improvement. Discussion: In conclusion, we have developed a framework, FLAP, for linking free-text UK addresses to the UPRN DB with good performance and usability in a real-world task.
| Type: | Article |
|---|---|
| Title: | FLAP: a framework for linking free-text addresses to the Ordnance Survey Unique Property Reference Number database |
| Location: | Switzerland |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.3389/fdgth.2023.1186208 |
| Publisher version: | http://dx.doi.org/10.3389/fdgth.2023.1186208 |
| Language: | English |
| Additional information: | © 2023 Zhang, Casey, Guellil, Suárez-Paniagua, Macrae, Marwick, Wu, Guthrie and Alex. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
| Keywords: | UPRN, Unique Property Reference Number, free-text address, machine learning, record linkage |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10184716 |
Archive Staff Only
 |
View Item |
