BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

286 related articles for article (PubMed ID: 34762064)

  • 21. The Influence of Media Coverage and Governmental Policies on Google Queries Related to COVID-19 Cutaneous Symptoms: Infodemiology Study.
    Huynh Dagher S; Lamé G; Hubiche T; Ezzedine K; Duong TA
    JMIR Public Health Surveill; 2021 Feb; 7(2):e25651. PubMed ID: 33513563
    [TBL] [Abstract][Full Text] [Related]  

  • 22. Predicting Smoking Prevalence in Japan Using Search Volumes in an Internet Search Engine: Infodemiology Study.
    Taira K; Itaya T; Fujita S
    J Med Internet Res; 2022 Dec; 24(12):e42619. PubMed ID: 36515993
    [TBL] [Abstract][Full Text] [Related]  

  • 23. Global Infodemiology of COVID-19: Analysis of Google Web Searches and Instagram Hashtags.
    Rovetta A; Bhagavathula AS
    J Med Internet Res; 2020 Aug; 22(8):e20673. PubMed ID: 32748790
    [TBL] [Abstract][Full Text] [Related]  

  • 24. Understanding Health Communication Through Google Trends and News Coverage for COVID-19: Multinational Study in Eight Countries.
    Ming WK; Huang F; Chen Q; Liang B; Jiao A; Liu T; Wu H; Akinwunmi B; Li J; Liu G; Zhang CJP; Huang J; Liu Q
    JMIR Public Health Surveill; 2021 Dec; 7(12):e26644. PubMed ID: 34591781
    [TBL] [Abstract][Full Text] [Related]  

  • 25. Predicting the Number of Suicides in Japan Using Internet Search Queries: Vector Autoregression Time Series Model.
    Taira K; Hosokawa R; Itatani T; Fujita S
    JMIR Public Health Surveill; 2021 Dec; 7(12):e34016. PubMed ID: 34823225
    [TBL] [Abstract][Full Text] [Related]  

  • 26. Understanding myocardial infarction trends during the early COVID-19 pandemic: an infodemiology study.
    Dzaye O; Duebgen M; Berning P; Graham G; Martin SS; Blaha MJ
    Intern Med J; 2021 Aug; 51(8):1328-1331. PubMed ID: 34213031
    [TBL] [Abstract][Full Text] [Related]  

  • 27. Ocular-symptoms-related Google Search Trends during the COVID-19 Pandemic in Europe.
    Mirza E; Mirza GD; Belviranli S; Oltulu R; Okka M
    Int Ophthalmol; 2021 Jun; 41(6):2213-2223. PubMed ID: 33725271
    [TBL] [Abstract][Full Text] [Related]  

  • 28. Explanation of hand, foot, and mouth disease cases in Japan using Google Trends before and during the COVID-19: infodemiology study.
    Niu Q; Liu J; Zhao Z; Onishi M; Kawaguchi A; Bandara A; Harada K; Aoyama T; Nagai-Tanima M
    BMC Infect Dis; 2022 Oct; 22(1):806. PubMed ID: 36309663
    [TBL] [Abstract][Full Text] [Related]  

  • 29. Assessment of the Impact of Media Coverage on COVID-19-Related Google Trends Data: Infodemiology Study.
    Sousa-Pinto B; Anto A; Czarlewski W; Anto JM; Fonseca JA; Bousquet J
    J Med Internet Res; 2020 Aug; 22(8):e19611. PubMed ID: 32530816
    [TBL] [Abstract][Full Text] [Related]  

  • 30. Regional Infoveillance of COVID-19 Case Rates: Analysis of Search-Engine Query Patterns.
    Cousins HC; Cousins CC; Harris A; Pasquale LR
    J Med Internet Res; 2020 Jul; 22(7):e19483. PubMed ID: 32692691
    [TBL] [Abstract][Full Text] [Related]  

  • 31. Impact of the COVID-19 pandemic on interest in renal diseases.
    Oto OA; Kardeş S; Guller N; Safak S; Dirim AB; Başhan Y; Demir E; Artan AS; Yazıcı H; Turkmen A
    Environ Sci Pollut Res Int; 2022 Jan; 29(1):711-718. PubMed ID: 34341920
    [TBL] [Abstract][Full Text] [Related]  

  • 32. Predicting COVID-19 Incidence Through Analysis of Google Trends Data in Iran: Data Mining and Deep Learning Pilot Study.
    Ayyoubzadeh SM; Ayyoubzadeh SM; Zahedi H; Ahmadi M; R Niakan Kalhori S
    JMIR Public Health Surveill; 2020 Apr; 6(2):e18828. PubMed ID: 32234709
    [TBL] [Abstract][Full Text] [Related]  

  • 33. Global Interest in Telehealth During COVID-19 Pandemic: An Analysis of Google Trends™.
    Arshad Ali S; Bin Arif T; Maab H; Baloch M; Manazir S; Jawed F; Ochani RK
    Cureus; 2020 Sep; 12(9):e10487. PubMed ID: 33083187
    [TBL] [Abstract][Full Text] [Related]  

  • 34. Association of the COVID-19 pandemic with Internet Search Volumes: A Google Trends
    Effenberger M; Kronbichler A; Shin JI; Mayer G; Tilg H; Perco P
    Int J Infect Dis; 2020 Jun; 95():192-197. PubMed ID: 32305520
    [TBL] [Abstract][Full Text] [Related]  

  • 35. Estimating the risk of SARS-CoV-2 deaths using a Markov switching-volatility model combined with heavy-tailed distributions for South Africa.
    Mthethwa N; Chifurira R; Chinhamu K
    BMC Public Health; 2022 Oct; 22(1):1873. PubMed ID: 36207700
    [TBL] [Abstract][Full Text] [Related]  

  • 36. Determining the nutritional immunity information-seeking behaviour during the COVID-19 pandemic in India: a Google Trends data analysis.
    Kushwaha S; Khanna P; Jain R; Srivastava R
    Public Health Nutr; 2021 Nov; 24(16):5338-5349. PubMed ID: 34348829
    [TBL] [Abstract][Full Text] [Related]  

  • 37. Investigating the Prevalence of Reactive Online Searching in the COVID-19 Pandemic: Infoveillance Study.
    Badell-Grau RA; Cuff JP; Kelly BP; Waller-Evans H; Lloyd-Evans E
    J Med Internet Res; 2020 Oct; 22(10):e19791. PubMed ID: 32915763
    [TBL] [Abstract][Full Text] [Related]  

  • 38. Tracking COVID-19 in Europe: Infodemiology Approach.
    Mavragani A
    JMIR Public Health Surveill; 2020 Apr; 6(2):e18941. PubMed ID: 32250957
    [TBL] [Abstract][Full Text] [Related]  

  • 39. Interest in dentistry in early months of the COVID-19 global pandemic: A Google Trends approach.
    Bağcı N; Peker I
    Health Info Libr J; 2022 Sep; 39(3):284-292. PubMed ID: 35166022
    [TBL] [Abstract][Full Text] [Related]  

  • 40. Information-Seeking Patterns During the COVID-19 Pandemic Across the United States: Longitudinal Analysis of Google Trends Data.
    Mangono T; Smittenaar P; Caplan Y; Huang VS; Sutermaster S; Kemp H; Sgaier SK
    J Med Internet Res; 2021 May; 23(5):e22933. PubMed ID: 33878015
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 15.