Published on in Vol 3, No 3 (2017): Jul-Sept

Attitudes of Crohn’s Disease Patients: Infodemiology Case Study and Sentiment Analysis of Facebook and Twitter Posts

Attitudes of Crohn’s Disease Patients: Infodemiology Case Study and Sentiment Analysis of Facebook and Twitter Posts

Attitudes of Crohn’s Disease Patients: Infodemiology Case Study and Sentiment Analysis of Facebook and Twitter Posts

Journals

  1. Mavragani A, Ochoa G. Infoveillance of infectious diseases in USA: STDs, tuberculosis, and hepatitis. Journal of Big Data 2018;5(1) View
  2. Pilozzi A, Huang X. Overcoming Alzheimer’s Disease Stigma by Leveraging Artificial Intelligence and Blockchain Technologies. Brain Sciences 2020;10(3):183 View
  3. Lombardo G, Fornacciari P, Mordonini M, Sani L, Tomaiuolo M. A combined approach for the analysis of support groups on Facebook - the case of patients of hidradenitis suppurativa. Multimedia Tools and Applications 2019;78(3):3321 View
  4. Sharma C, Whittle S, Haghighi P, Burstein F, Keen H. Sentiment analysis of social media posts on pharmacotherapy: A scoping review. Pharmacology Research & Perspectives 2020;8(5) View
  5. Mavragani A, Ochoa G. Google Trends in Infodemiology and Infoveillance: Methodology Framework. JMIR Public Health and Surveillance 2019;5(2):e13439 View
  6. Salzmann-Erikson M, Eriksson H. A descriptive statistical analysis of volume, visibility and attitudes regarding nursing and care robots in social media. Contemporary Nurse 2018;54(1):88 View
  7. Yin Z, Sulieman L, Malin B. A systematic literature review of machine learning in online personal health data. Journal of the American Medical Informatics Association 2019;26(6):561 View
  8. Barros J, Duggan J, Rebholz-Schuhmann D. The Application of Internet-Based Sources for Public Health Surveillance (Infoveillance): Systematic Review. Journal of Medical Internet Research 2020;22(3):e13680 View
  9. Mavragani A. Infodemiology and Infoveillance: Scoping Review. Journal of Medical Internet Research 2020;22(4):e16206 View
  10. García-Díaz J, Cánovas-García M, Valencia-García R. Ontology-driven aspect-based sentiment analysis classification: An infodemiological case study regarding infectious diseases in Latin America. Future Generation Computer Systems 2020;112:641 View
  11. Mavragani A, Ochoa G, Tsagarakis K. Assessing the Methods, Tools, and Statistical Approaches in Google Trends Research: Systematic Review. Journal of Medical Internet Research 2018;20(11):e270 View
  12. Zunic A, Corcoran P, Spasic I. Sentiment Analysis in Health and Well-Being: Systematic Review. JMIR Medical Informatics 2020;8(1):e16023 View
  13. Rovetta A, Bhagavathula A. COVID-19-Related Web Search Behaviors and Infodemic Attitudes in Italy: Infodemiological Study. JMIR Public Health and Surveillance 2020;6(2):e19374 View
  14. Gbashi S, Adebo O, Doorsamy W, Njobeh P. Systematic Delineation of Media Polarity on COVID-19 Vaccines in Africa: Computational Linguistic Modeling Study. JMIR Medical Informatics 2021;9(3):e22916 View
  15. Dubey A. The Resurgence of Cyber Racism During the COVID-19 Pandemic and its Aftereffects: Analysis of Sentiments and Emotions in Tweets. JMIR Public Health and Surveillance 2020;6(4):e19833 View
  16. Momynaliev K, Khoperskay L, Pshenichnaya N, Abuova G, Akimkin V. Infodemiological study of coronavirus epidemic using Google Trends in Central Asian Republics of Kazakhstan, Kyrgyzstan, Uzbekistan, Tajikistan. Medical alphabet 2021;(34):47 View
  17. D’Souza R, Hooten W, Murad M. A Proposed Approach for Conducting Studies That Use Data From Social Media Platforms. Mayo Clinic Proceedings 2021;96(8):2218 View
  18. Cury G, Takamune D, Herrerias G, Rivera-Sequeiros A, de Barros J, Baima J, Saad-Hossne R, Sassaki L. Clinical and Psychological Factors Associated with Addiction and Compensatory Use of Facebook Among Patients with Inflammatory Bowel Disease: A Cross-Sectional Study. International Journal of General Medicine 2022;Volume 15:1447 View
  19. Lee K, Song S. Developing insights from the collective voice of target users in Twitter. Journal of Big Data 2022;9(1) View
  20. Stemmer M, Parmet Y, Ravid G. Identifying Patients With Inflammatory Bowel Disease on Twitter and Learning From Their Personal Experience: Retrospective Cohort Study. Journal of Medical Internet Research 2022;24(8):e29186 View
  21. Ji M, Xie W, Huang R, Qian X. Automatic Diagnosis of Mental Healthcare Information Actionability: Developing Binary Classifiers. International Journal of Environmental Research and Public Health 2021;18(20):10743 View
  22. Galbraith E, Li J, Rio-Vilas V, Convertino M. In.To. COVID-19 socio-epidemiological co-causality. Scientific Reports 2022;12(1) View
  23. Chen D, Fulmer C, Gordon I, Syed S, Stidham R, Vande Casteele N, Qin Y, Falloon K, Cohen B, Wyllie R, Rieder F. Application of Artificial Intelligence to Clinical Practice in Inflammatory Bowel Disease – What the Clinician Needs to Know. Journal of Crohn's and Colitis 2022;16(3):460 View
  24. Lim L, Lim A, Fong K, Lee C. Sentiments Regarding COVID-19 Vaccination among Graduate Students in Singapore. Vaccines 2021;9(10):1141 View
  25. Sharaf M, Hemdan E, El-Sayed A, El-Bahnasawy N. An efficient hybrid stock trend prediction system during COVID-19 pandemic based on stacked-LSTM and news sentiment analysis. Multimedia Tools and Applications 2023;82(16):23945 View
  26. Kayıkçı Ş. SenDemonNet: sentiment analysis for demonetization tweets using heuristic deep neural network. Multimedia Tools and Applications 2022;81(8):11341 View
  27. Zhang S, Chu-ke C, Kim H, Jing C, Pandian S. Public View of Public Health Emergencies Based on Artificial Intelligence Data. Journal of Environmental and Public Health 2022;2022:1 View
  28. Kinariwala S, Deshmukh S. Short text topic modelling using local and global word-context semantic correlation. Multimedia Tools and Applications 2023;82(17):26411 View
  29. Unnikrishnan R, S. S, V.S. A. Efficient parameter tuning of neural foundation models for drug perspective prediction from unstructured socio-medical data. Engineering Applications of Artificial Intelligence 2023;123:106214 View
  30. Stemmer M, Parmet Y, Ravid G. What are IBD Patients Talking About on Twitter? Using Natural Language Understanding to Investigate Patients’ Tweets. SN Computer Science 2023;4(4) View
  31. Belagur H, Reddy N, Krishna P, Tumuluri R. Cross-modal multi-headed attention for long multimodal conversations. Multimedia Tools and Applications 2023;82(29):45679 View
  32. Escobar-Grisales D, Vásquez-Correa J, Orozco-Arroyave J. Evaluation of effectiveness in conversations between humans and chatbots using parallel convolutional neural networks with multiple temporal resolutions. Multimedia Tools and Applications 2024;83(2):5473 View
  33. Comacchio C, Cesco M, Martinelli R, Garzitto M, Bianchi R, Innocente N, Sozio E, Tascini C, Balestrieri M, Colizzi M. Psychological factors associated with vaccination hesitancy: an observational study of patients hospitalized for COVID-19 in a later phase of the pandemic in Italy. Frontiers in Psychiatry 2023;14 View
  34. Al-Qablan T, Mohd Noor M, Al-Betar M, Khader A. A survey on sentiment analysis and its applications. Neural Computing and Applications 2023;35(29):21567 View
  35. Miao X, Zhang X, Zhang H. Low-rank tensor fusion and self-supervised multi-task multimodal sentiment analysis. Multimedia Tools and Applications 2024 View
  36. Wang Y, Wang L, Ma W, Zhao H, Han X, Zhao X. Development of a novel dynamic nosocomial infection risk management method for COVID-19 in outpatient settings. BMC Infectious Diseases 2024;24(1) View

Books/Policy Documents

  1. Apolinario-Arzube Ó, Garcí­a-Dí­az J, Pinto S, Luna-Aveiga H, Medina-Moreira J, Gómez-Berbis J, Valencia-Garcia R, Estrade-Cabrera J. Applied Informatics and Cybernetics in Intelligent Systems. View
  2. Stemmer M, Parmet Y, Ravid G. ICT for Health, Accessibility and Wellbeing. View