L Toubiana1, N Griffon2. 1. Dr. Laurent Toubiana, PhD, INSERM UMRS 1142 "LIMICS", 15, rue de l'École de Médecine, 75006 Paris, France, Tel: +33 1 44 27 91 97, E-mail: Laurent.toubiana@inserm.fr. 2. Dr. Nicolas Griffon, Unité d'Informatique Médicale, CHU de Rouen, 1 rue de Germont, 76031, Rouen, France, Tel. +33 6 42 25 44 11, E-mail: nicolas.griffon@chu-rouen.fr.
Abstract
OBJECTIVES: Summarize excellent current research published in 2015 in the field of Public Health and Epidemiology Informatics. METHODS: The complete 2015 literature concerning public health and epidemiology informatics has been 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 allow the editorial team an enlightened selection of the best papers. RESULTS: Among the 1,272 references retrieved from PubMed and Web of Science, three were finally selected as best papers. The first one presents a language agnostic approach for epidemic event detection in news articles. The second paper describes a system using big health data gathered by a statewide system to forecast emergency department visits. The last paper proposes a rather original approach that uses machine learning to solve the old issue of outbreak detection and prediction. CONCLUSIONS: The increasing availability of data, now directly from health systems, will probably lead to a boom in public health surveillance systems and in large-scale epidemiologic studies.
OBJECTIVES: Summarize excellent current research published in 2015 in the field of Public Health and Epidemiology Informatics. METHODS: The complete 2015 literature concerning public health and epidemiology informatics has been 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 allow the editorial team an enlightened selection of the best papers. RESULTS: Among the 1,272 references retrieved from PubMed and Web of Science, three were finally selected as best papers. The first one presents a language agnostic approach for epidemic event detection in news articles. The second paper describes a system using big health data gathered by a statewide system to forecast emergency department visits. The last paper proposes a rather original approach that uses machine learning to solve the old issue of outbreak detection and prediction. CONCLUSIONS: The increasing availability of data, now directly from health systems, will probably lead to a boom in public health surveillance systems and in large-scale epidemiologic studies.
Keywords:
International Medical Informatics Association; Public health; epidemiology; health information systems; medical informatics
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