Knowledge discovery and visualization in antimicrobial resistance surveillance systems: a scoping review

Safdari, Reza; Ghazisaeedi, Marjan; Masoumi-Asl, Hossein; Rezaei‐Hachesu, Peyman; Mirnia, Kayvan; Samad‐Soltani, Taha

doi:10.1007/s10462-018-9659-6

Cited by 3 publications

(1 citation statement)

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“…This is why an increasing attention has been devoted to the design of dashboards, i.e., systems able to pre-process and integrate heterogeneous data, and present a synthetic view of the information to the user - in the form, for instance, of a trend analysis or of the geographical distribution of threads. Such information could then be used to better guide policy making 48 , e.g., to interpret the data with an intersectoral approach (human, animal, environment), and to assess and predict the risk of transmission. Beyond many older works, which are reviewed in 48 , it is worth highlighting a recent paper describing the design and evaluation of such a dashboard in northwestern Iranian hospitals 49 , for going beyond a pure research work and being an example of an actual deployed solution.…”

Section: Resultsmentioning

confidence: 99%

Public Health and Epidemiology Informatics: Can Artificial Intelligence Help Future Global Challenges? An Overview of Antimicrobial Resistance and Impact of Climate Change in Disease Epidemiology

2019

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Objectives: To provide an oveiview of the current application of artificial intelligence (AI) in the field of public health and epidemiology, with a special focus on antimicrobial resistance and the impact of climate change in disease epidemiology. Both topics are of vital importance and were included in the “Ten threats to global health in 2019“ report published by the World Health Organization. Methods: We analysed publications that appeared in the last two years, between January 2017 and October 2018. Papers were searched using Google Scholar with the following keywords: public health, epidemiology, machine learning, data analytics, artificial intelligence, disease surveillance, climate change, antimicrobial resistance, and combinations thereof. Selected articles were organised by theme. Results: In spite of a large interest in AI generated both within and outside the scientific community, and of the many opinions pointing towards the importance of a better use of data in public health, few papers have been published on the selected topics in the last two years. We identify several potential reasons, including the complexity of the integration of heterogeneous data, and the lack of sound and unbiased validation procedures. Conclusions: As there is a better comprehension of AI and more funding available, artificial intelligence will become not only the centre of attention in informatics, but more importantly the source of innovative solutions for public health.

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Section: Resultsmentioning

confidence: 99%