Case Studies on the Use of Sentiment Analysis to Assess the Effectiveness and Safety of Health Technologies: A Scoping Review

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.

[1]  Seong Joon Yoo,et al.  Senti-lexicon and improved Naïve Bayes algorithms for sentiment analysis of restaurant reviews , 2012, Expert Syst. Appl..

[2]  Sharon E. Straus,et al.  A scoping review on the conduct and reporting of scoping reviews , 2016, BMC Medical Research Methodology.

[3]  Ernesto Iadanza,et al.  Evidence-based approach to medical equipment maintenance monitoring , 2017 .

[4]  Nikolaos Pandis,et al.  “My Invisalign experience”: content, metrics and comment sentiment analysis of the most popular patient testimonials on YouTube , 2018, Progress in orthodontics.

[5]  Xinyang Jiang,et al.  Sentiment Analysis of an Online Breast Cancer Support Group: Communicating about Tamoxifen , 2018, Health communication.

[6]  Philip Treleaven,et al.  Twitter Sentiment Analysis , 2015, ArXiv.

[7]  Damminda Alahakoon,et al.  Machine learning to support social media empowered patients in cancer care and cancer treatment decisions , 2018, PloS one.

[8]  Philip M. Massey,et al.  HPV vaccine, Twitter, and gay, bisexual and other men who have sex with men. , 2020, Health promotion international.

[9]  D. Noll,et al.  Twitter analysis of the orthodontic patient experience with braces vs Invisalign. , 2017, The Angle orthodontist.

[10]  Marco Roccetti,et al.  On the interpretation of the effects of the Infliximab treatment on Crohn’s disease patients from Facebook posts: a human vs. machine comparison , 2017, Network Modeling Analysis in Health Informatics and Bioinformatics.

[11]  Fabrício Benevenuto,et al.  Comparing and combining sentiment analysis methods , 2013, COSN '13.

[12]  Yuan Cao,et al.  Understanding the perceptions of Chinese women of the commercially available domestic and imported HPV vaccine: A semantic network analysis. , 2020, Vaccine.

[13]  Ernesto Iadanza,et al.  Medical devices in Sub-Saharan Africa: optimal assistance via a computerized maintenance management system (CMMS) in Benin , 2019 .

[14]  Falls Church,et al.  HTA 101 INTRODUCTION TO HEALTH TECHNOLOGY ASSESSMENT , 2004 .

[15]  D. Parker,et al.  Guidance for conducting systematic scoping reviews , 2015, International journal of evidence-based healthcare.

[16]  Ernesto Iadanza,et al.  Evidence-based medical equipment management: a convenient implementation , 2019, Medical & Biological Engineering & Computing.

[17]  Yixiong Pan,et al.  SPEECH EMOTION RECOGNITION USING SUPPORT VECTOR MACHINE , 2010 .

[18]  H. Arksey,et al.  Scoping studies: towards a methodological framework , 2005 .

[19]  Loo-Nin Teow,et al.  Troll detection by domain-adapting sentiment analysis , 2015, 2015 18th International Conference on Information Fusion (Fusion).

[20]  Jingcheng Du,et al.  Optimization on machine learning based approaches for sentiment analysis on HPV vaccines related tweets , 2017, Journal of Biomedical Semantics.

[21]  L Pecchia,et al.  Health Technology Assessment and Biomedical Engineering: Global trends, gaps and opportunities. , 2019, Medical engineering & physics.

[22]  Luis Alfonso Ureña López,et al.  How do we talk about doctors and drugs? Sentiment analysis in forums expressing opinions for medical domain , 2019, Artif. Intell. Medicine.

[23]  Federica Fornaciari,et al.  Semantic Network Analysis , 2016 .

[24]  Altug Akay,et al.  Network-Based Modeling and Intelligent Data Mining of Social Media for Improving Care , 2015, IEEE Journal of Biomedical and Health Informatics.

[25]  Hongfei Yan,et al.  Jointly Modeling Aspects and Opinions with a MaxEnt-LDA Hybrid , 2010, EMNLP.

[26]  Ara Darzi,et al.  Sentiment Analysis of Health Care Tweets: Review of the Methods Used , 2018, JMIR public health and surveillance.

[27]  G. Zimet,et al.  A natural language processing framework to analyse the opinions on HPV vaccination reflected in twitter over 10 years (2008 - 2017) , 2019, Human vaccines & immunotherapeutics.

[28]  J. McGowan,et al.  PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation , 2018, Annals of Internal Medicine.

[29]  Vinaya Sawant,et al.  Comparison of Traditional Machine Learning and Deep Learning Approaches for Sentiment Analysis , 2020 .

[30]  Ling Zhang,et al.  Utilizing Twitter data for analysis of chemotherapy , 2018, Int. J. Medical Informatics.

[31]  D. Levac,et al.  Scoping studies: advancing the methodology , 2010, Implementation science : IS.