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Linkage of Hospital Records and Death Certificates by a Search Engine and Machine Learning
INTRODUCTION: Vital status is of central importance to hospital clinical research. However, hospital information systems record only in-hospital death information. Recently, the French government released a publicly available dataset containing death-certificate data for over 25 million individuals....
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7935495/ https://www.ncbi.nlm.nih.gov/pubmed/33709061 http://dx.doi.org/10.1093/jamiaopen/ooab005 |
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author | Cossin, Sebastien Diouf, Serigne Griffier, Romain Le Barrois d’Orgeval, Philippine Diallo, Gayo Jouhet, Vianney |
author_facet | Cossin, Sebastien Diouf, Serigne Griffier, Romain Le Barrois d’Orgeval, Philippine Diallo, Gayo Jouhet, Vianney |
author_sort | Cossin, Sebastien |
collection | PubMed |
description | INTRODUCTION: Vital status is of central importance to hospital clinical research. However, hospital information systems record only in-hospital death information. Recently, the French government released a publicly available dataset containing death-certificate data for over 25 million individuals. The objective of this study was to link French death certificates to the Bordeaux University Hospital records to complete the vital status information. MATERIALS AND METHODS: Our linkage strategy was composed of a search engine to reduce the number of comparisons and machine-learning algorithms. The overall pipeline was evaluated by assembling a file containing 3,565 in-hospital deaths and 15,000 alive persons. RESULTS: The recall and precision of our linkage strategy were 97.5% and 99.97% for the upper threshold and 99.4% and 98.9% for the lower threshold, respectively. CONCLUSION: In this study, we demonstrated the feasibility of accurately linking hospital records with death certificates using a search engine and machine learning. |
format | Online Article Text |
id | pubmed-7935495 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-79354952021-03-10 Linkage of Hospital Records and Death Certificates by a Search Engine and Machine Learning Cossin, Sebastien Diouf, Serigne Griffier, Romain Le Barrois d’Orgeval, Philippine Diallo, Gayo Jouhet, Vianney JAMIA Open Application Notes INTRODUCTION: Vital status is of central importance to hospital clinical research. However, hospital information systems record only in-hospital death information. Recently, the French government released a publicly available dataset containing death-certificate data for over 25 million individuals. The objective of this study was to link French death certificates to the Bordeaux University Hospital records to complete the vital status information. MATERIALS AND METHODS: Our linkage strategy was composed of a search engine to reduce the number of comparisons and machine-learning algorithms. The overall pipeline was evaluated by assembling a file containing 3,565 in-hospital deaths and 15,000 alive persons. RESULTS: The recall and precision of our linkage strategy were 97.5% and 99.97% for the upper threshold and 99.4% and 98.9% for the lower threshold, respectively. CONCLUSION: In this study, we demonstrated the feasibility of accurately linking hospital records with death certificates using a search engine and machine learning. Oxford University Press 2021-03-01 /pmc/articles/PMC7935495/ /pubmed/33709061 http://dx.doi.org/10.1093/jamiaopen/ooab005 Text en © The Author(s) 2021. Published by Oxford University Press on behalf of the American Medical Informatics Association. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Application Notes Cossin, Sebastien Diouf, Serigne Griffier, Romain Le Barrois d’Orgeval, Philippine Diallo, Gayo Jouhet, Vianney Linkage of Hospital Records and Death Certificates by a Search Engine and Machine Learning |
title | Linkage of Hospital Records and Death Certificates by a Search Engine and Machine Learning |
title_full | Linkage of Hospital Records and Death Certificates by a Search Engine and Machine Learning |
title_fullStr | Linkage of Hospital Records and Death Certificates by a Search Engine and Machine Learning |
title_full_unstemmed | Linkage of Hospital Records and Death Certificates by a Search Engine and Machine Learning |
title_short | Linkage of Hospital Records and Death Certificates by a Search Engine and Machine Learning |
title_sort | linkage of hospital records and death certificates by a search engine and machine learning |
topic | Application Notes |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7935495/ https://www.ncbi.nlm.nih.gov/pubmed/33709061 http://dx.doi.org/10.1093/jamiaopen/ooab005 |
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