A probabilistic semantic analysis of eHealth scientific literature

What is it about?

eHealth emerged as an interdisciplinary research area about 70 years ago. This work employs probabilistic techniques to semantically analyze scientific literature related to the field of eHealth in order to identify topics and trends and discuss their comparative evolution. A total of 30,425 records on eHealth were retrieved from PubMed (all records till 31 Dec 2017, search on 08 May 2018) and 23,988 of these were included to the study corpus. eHealth domain shows a growth higher than the growth of the entire PubMed corpus, with a mean increase of eHealth corpus proportion of about 7% per year for the last 20 years. Probabilistic topics modeling identified 100 meaningful topics, which were organized by the authors in 9 different categories: general; service model; disease; medical specialty; behavior and lifestyle; education; technology; evaluation; and regulatory issues. Trends analysis shows a continuous shift in focus. Early emphasis on medical image transmission and system integration has been replaced with hypes on standards, wearables and sensor devices, now giving way to mobile applications, social media and data analytics. Attention on disease is also shifting, from an initial popularity of surgery, trauma and acute heart disease, to the emergence of chronic disease support, and the recent attention to cancer, infectious disease, mental disorders, pediatrics and perinatal care; most interesting the current swift increase in research related to lifestyle and behavior change. The steady growth of all topics related to assessment and various systematic evaluation techniques indicates a maturing research field that moves towards real world application.

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Photo by Stephen Dawson on Unsplash

Photo by Stephen Dawson on Unsplash

Why is it important?

The probabilistic semantic analysis, that was employed to identify topics and trends in the scientific literature, is a good technique to perform a fast review of the literature and an unique opportunity to review literature in case where the number of articles is huge.

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