Public Health and Epidemiology Informatics.
OBJECTIVE
The aim of this manuscript is to provide a brief overview of the scientific challenges that should be addressed in order to unlock the full potential of using data from a general point of view, as well as to present some ideas that could help answer specific needs for data understanding in the field of health sciences and epidemiology.


METHODS
A survey of uses and challenges of big data analyses for medicine and public health was conducted. The first part of the paper focuses on big data techniques, algorithms, and statistical approaches to identify patterns in data. The second part describes some cutting-edge applications of analyses and predictive modeling in public health.


RESULTS
In recent years, we witnessed a revolution regarding the nature, collection, and availability of data in general. This was especially striking in the health sector and particularly in the field of epidemiology. Data derives from a large variety of sources, e.g. clinical settings, billing claims, care scheduling, drug usage, web based search queries, and Tweets.


CONCLUSIONS
The exploitation of the information (data mining, artificial intelligence) relevant to these data has become one of the most promising as well challenging tasks from societal and scientific viewpoints in order to leverage the information available and making public health more efficient.
Prof. Antoine Flahault, Chair Louis Jeantet in Public Health, Director of the Institute of Global Health, Faculté de Médecine, Université de Genève, Campus Biotech, Cheimin des Mines 9, 1202 Geneva, Switzerland, E-mail: antoine.flahault@unige.ch.
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Flahault
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Bar-Hen
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Paragios
D001185:Artificial Intelligence; D057225:Data Mining; D066264:Datasets as Topic; D004812:Epidemiologic Methods; D006801:Humans; D017891:Pharmacoepidemiology / Q000379:methods*; D011159:Population Surveillance / Q000379:methods*; D011634:Public Health
Big data; data analytics; disease surveillance; learning machine; pharmacoepidemiology