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Case studies on the use of sentiment analysis to assess the effectiveness and safety of health technologies : a scoping review
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Polisena, Julie, Andellini, Martina, Salerno, Piergiorgio, Borsci, Simone, Pecchia, Leandro and Iadanza, Ernesto (2021) Case studies on the use of sentiment analysis to assess the effectiveness and safety of health technologies : a scoping review. IEEE Access, 9 . pp. 66043-66051. doi:10.1109/ACCESS.2021.3076356
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WRAP-Case-studies-use-sentiment-analysis-safety-health-technology-scoping-review-2021.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (1226Kb) | Preview |
Official URL: http://dx.doi.org/10.1109/ACCESS.2021.3076356
Abstract
A health technology assessment (HTA) is commonly defined as a multidisciplinary approach used to evaluate medical, social, economic, and ethical issues related to the use of a health technology in a systematic, transparent, unbiased, robust manner. To help inform HTA recommendations, the surveillance of social media platforms can provide important insights to the clinical community and to decision makers on the effectiveness and safety of the use of health technologies on a patient. A scoping review of the published literature was performed to gain some insight on the accuracy and automation of sentiment analysis (SA) used to assess public opinion on the use of health technologies. A literature search of major databases was conducted. The main search concepts were SA, social media, and patient perspective. Among the 1,776 unique citations identified, 12 studies that described the use of SA methods to evaluate public opinion on or experiences with the use of health technologies as posted on social media platforms were included. The SA methods used were either lexicon-or machine learning-based. Two studies focused on medical devices, three examined HPV vaccination, and the remaining studies targeted drug therapies. Due to the limitations and inherent differences among SA tools, the outcomes of these applications should be considered exploratory. The results of our study can initiate discussions on how the automation of algorithms to interpret public opinion of health technologies should be further developed to optimize the use of data available on social media.
| Item Type: | Journal Article | ||||||
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| Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software R Medicine > R Medicine (General) |
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| Divisions: | Faculty of Science > Engineering | ||||||
| Library of Congress Subject Headings (LCSH): | Medical technology, Medical technology -- Safety measures, Medical technology -- Evaluation, Sentiment analysis | ||||||
| Journal or Publication Title: | IEEE Access | ||||||
| Publisher: | IEEE | ||||||
| ISSN: | 2169-3536 | ||||||
| Official Date: | 28 April 2021 | ||||||
| Dates: |
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| Volume: | 9 | ||||||
| Page Range: | pp. 66043-66051 | ||||||
| DOI: | 10.1109/ACCESS.2021.3076356 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | Published | ||||||
| Access rights to Published version: | Open Access | ||||||
| RIOXX Funder/Project Grant: |
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