Artificial Intelligence in Public Health and Epidemiology
| dc.rights.license | open | en_US |
| hal.structure.identifier | Statistics In System biology and Translational Medicine [SISTM] | |
| hal.structure.identifier | Bordeaux population health [BPH] | |
| dc.contributor.author | THIEBAUT, Rodolphe | |
| hal.structure.identifier | Bordeaux population health [BPH] | |
| dc.contributor.author | THIESSARD, Frantz | |
| dc.date.accessioned | 2021-01-05T15:56:20Z | |
| dc.date.available | 2021-01-05T15:56:20Z | |
| dc.date.issued | 2018-08 | |
| dc.identifier.issn | 2364-0502 (Electronic) 0943-4747 (Linking) | en_US |
| dc.identifier.uri | https://oskar-bordeaux.fr/handle/20.500.12278/23678 | |
| dc.description.abstractEn | OBJECTIVES: To introduce and summarize current research in the field of Public Health and Epidemiology Informatics. METHODS: The 2017 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 14 candidate best papers. These papers were then peer-reviewed by external reviewers to provide the editorial team with an enlightened vision to select the best papers. RESULTS: Among the 843 references retrieved from PubMed and Web of Science, two were finally selected as best papers. The first one analyzes the relationship between the disease, social/mass media, and public emotions to understand public overreaction (leading to a noticeable reduction of social and economic activities) in the context of a nation-wide outbreak of Middle East Respiratory Syndrome (MERS) in Korea in 2015. The second paper concerns a new methodology to de-identify patient notes in electronic health records based on artificial neural networks that outperformed existing methods. CONCLUSIONS: Surveillance is still a productive topic in public health informatics but other very important topics in Public Health are appearing. For example, the use of artificial intelligence approaches is increasing. | |
| dc.language.iso | EN | en_US |
| dc.subject.en | SISTM | |
| dc.subject.en | ERIAS | |
| dc.title.en | Artificial Intelligence in Public Health and Epidemiology | |
| dc.title.alternative | Yearb Med Inform | en_US |
| dc.type | Article de revue | en_US |
| dc.identifier.doi | 10.1055/s-0038-1667082 | en_US |
| dc.subject.hal | Sciences du Vivant [q-bio]/Santé publique et épidémiologie | en_US |
| dc.identifier.pubmed | 30157525 | en_US |
| bordeaux.journal | IMIA Yearbook of Medical Informatics | en_US |
| bordeaux.page | 207-210 | en_US |
| bordeaux.volume | 27 | en_US |
| bordeaux.hal.laboratories | Bordeaux Population Health Research Center (BPH) - UMR 1219 | en_US |
| bordeaux.issue | 1 | en_US |
| bordeaux.institution | Université de Bordeaux | en_US |
| bordeaux.team | SISTM_BPH | |
| bordeaux.peerReviewed | oui | en_US |
| bordeaux.inpress | non | en_US |
| hal.identifier | hal-03160811 | |
| hal.version | 1 | |
| hal.date.transferred | 2021-03-05T14:14:18Z | |
| hal.export | true | |
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