Cargando…
Artificial Intelligence for Surveillance in Public Health
Objectives : To introduce and summarize current research in the field of Public Health and Epidemiology Informatics. Methods : The 2018 literature concerning public health and epidemiology informatics was searched in PubMed and Web of Science, and the returned references were reviewed by the two sec...
Autores principales: | , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Georg Thieme Verlag KG
2019
|
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6697516/ https://www.ncbi.nlm.nih.gov/pubmed/31419837 http://dx.doi.org/10.1055/s-0039-1677939 |
_version_ | 1783444398283948032 |
---|---|
author | Thiébaut, Rodolphe Cossin, Sébastien |
author_facet | Thiébaut, Rodolphe Cossin, Sébastien |
author_sort | Thiébaut, Rodolphe |
collection | PubMed |
description | Objectives : To introduce and summarize current research in the field of Public Health and Epidemiology Informatics. Methods : The 2018 literature concerning public health and epidemiology informatics was searched in PubMed and Web of Science, and the returned references were reviewed by the two section editors to select 15 candidate best papers. These papers were then peer-reviewed by external reviewers to give the editorial team an enlightened selection of the best papers. Results : Among the 805 references retrieved from PubMed and Web of Science, three were finally selected as best papers. All three papers are about surveillance using digital tools. One study is about the surveillance of flu, another about emerging animal infectious diseases and the last one is about foodborne illness. The sources of information are Google news, Twitter, and Yelp restaurant reviews. Machine learning approaches are most often used to detect signals. Conclusions : Surveillance is a central topic in public health informatics with the growing use of machine learning approaches in regards of the size and complexity of data. The evaluation of the approaches developed remains a serious challenge. |
format | Online Article Text |
id | pubmed-6697516 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Georg Thieme Verlag KG |
record_format | MEDLINE/PubMed |
spelling | pubmed-66975162019-08-19 Artificial Intelligence for Surveillance in Public Health Thiébaut, Rodolphe Cossin, Sébastien Yearb Med Inform Objectives : To introduce and summarize current research in the field of Public Health and Epidemiology Informatics. Methods : The 2018 literature concerning public health and epidemiology informatics was searched in PubMed and Web of Science, and the returned references were reviewed by the two section editors to select 15 candidate best papers. These papers were then peer-reviewed by external reviewers to give the editorial team an enlightened selection of the best papers. Results : Among the 805 references retrieved from PubMed and Web of Science, three were finally selected as best papers. All three papers are about surveillance using digital tools. One study is about the surveillance of flu, another about emerging animal infectious diseases and the last one is about foodborne illness. The sources of information are Google news, Twitter, and Yelp restaurant reviews. Machine learning approaches are most often used to detect signals. Conclusions : Surveillance is a central topic in public health informatics with the growing use of machine learning approaches in regards of the size and complexity of data. The evaluation of the approaches developed remains a serious challenge. Georg Thieme Verlag KG 2019-08 2019-08-16 /pmc/articles/PMC6697516/ /pubmed/31419837 http://dx.doi.org/10.1055/s-0039-1677939 Text en https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License, which permits unrestricted reproduction and distribution, for non-commercial purposes only; and use and reproduction, but not distribution, of adapted material for non-commercial purposes only, provided the original work is properly cited. |
spellingShingle | Thiébaut, Rodolphe Cossin, Sébastien Artificial Intelligence for Surveillance in Public Health |
title | Artificial Intelligence for Surveillance in Public Health |
title_full | Artificial Intelligence for Surveillance in Public Health |
title_fullStr | Artificial Intelligence for Surveillance in Public Health |
title_full_unstemmed | Artificial Intelligence for Surveillance in Public Health |
title_short | Artificial Intelligence for Surveillance in Public Health |
title_sort | artificial intelligence for surveillance in public health |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6697516/ https://www.ncbi.nlm.nih.gov/pubmed/31419837 http://dx.doi.org/10.1055/s-0039-1677939 |
work_keys_str_mv | AT thiebautrodolphe artificialintelligenceforsurveillanceinpublichealth AT cossinsebastien artificialintelligenceforsurveillanceinpublichealth AT artificialintelligenceforsurveillanceinpublichealth |