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Citing this Article

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Published on 10.05.16 in Vol 18, No 5 (2016): May

This paper is in the following e-collection/theme issue:

Works citing "The Voice of Chinese Health Consumers: A Text Mining Approach to Web-Based Physician Reviews"

Haijing Hao, Kunpeng Zhang

J Med Internet Res 2016 (May 10); 18(5):e108 

According to Crossref, the following articles are citing this article (DOI 10.2196/jmir.4430):

(note that this is only a small subset of citations)

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  29. Deng Z, Hong Z, Zhang W, Evans R, Chen Y. The Effect of Online Effort and Reputation of Physicians on Patients’ Choice: 3-Wave Data Analysis of China’s Good Doctor Website. Journal of Medical Internet Research 2019;21(3):e10170
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  31. Hao H, Zhang K, Wang W, Gao G. A tale of two countries: International comparison of online doctor reviews between China and the United States. International Journal of Medical Informatics 2017;99:37
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  33. TOPLU S, CANGÜR . Text Mining Method in the Field of Health. Konuralp Tıp Dergisi 2020;12(2):236
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  35. Shah AM, Yan X, Tariq S, Ali M. What patients like or dislike in physicians: Analyzing drivers of patient satisfaction and dissatisfaction using a digital topic modeling approach. Information Processing & Management 2021;58(3):102516
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  36. Wu QL, Tang L. What Satisfies Parents of Pediatric Patients in China: A Grounded Theory Building Analysis of Online Physician Reviews. Health Communication 2022;37(10):1329
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  37. Khanbhai M, Anyadi P, Symons J, Flott K, Darzi A, Mayer E. Applying natural language processing and machine learning techniques to patient experience feedback: a systematic review. BMJ Health & Care Informatics 2021;28(1):e100262
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  38. Shah AM, Yan X, Qayyum A, Naqvi RA, Shah SJ. Mining topic and sentiment dynamics in physician rating websites during the early wave of the COVID-19 pandemic: Machine learning approach. International Journal of Medical Informatics 2021;149:104434
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  39. Gong Y, Wang H, Xia Q, Zheng L, Shi Y. Factors that determine a Patient's willingness to physician selection in online healthcare communities: A trust theory perspective. Technology in Society 2021;64:101510
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  40. Fairie P, Zhang Z, D'Souza AG, Walsh T, Quan H, Santana MJ. Categorising patient concerns using natural language processing techniques. BMJ Health & Care Informatics 2021;28(1):e100274
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  41. Wan Y, Peng Z, Wang Y, Zhang Y, Gao J, Ma B. Influencing factors and mechanism of doctor consultation volume on online medical consultation platforms based on physician review analysis. Internet Research 2021;31(6):2055
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  42. Hu Y, Zhou H, Chen Y, Yao J, Su J. The influence of patient-generated reviews and doctor-patient relationship on online consultations in China. Electronic Commerce Research 2023;23(2):1115
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  43. Saifee DH, Hudnall M, Raja U. Physician Gender, Patient Risk, and Web-Based Reviews: Longitudinal Study of the Relationship Between Physicians’ Gender and Their Web-Based Reviews. Journal of Medical Internet Research 2022;24(4):e31659
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  44. Liu J, Gao L. Research on the Characteristics and Usefulness of User Reviews of Online Mental Health Consultation Services: A Content Analysis. Healthcare 2021;9(9):1111
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  45. Hsu Y, Chiu Y, Wang J, Liu H. Impacts of physician promotion on the online healthcare community: Using a difference-in-difference approach. DIGITAL HEALTH 2022;8:205520762211063
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  46. Hsu Y, Duan R, Chiu Y, Wang J. Understanding the Inequality of Web Traffic and Engagement in Online Healthcare Communities. Frontiers in Public Health 2022;10
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  47. Wei X, Hsu Y. Extracting Additional Influences From Physician Profiles With Topic Modeling: Impact on Ratings and Page Views in Online Healthcare Communities. Frontiers in Psychology 2022;13
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  48. Yin R, Tian R, Wu J, Gan F. Exploring the Factors Associated with Mental Health Attitude in China: A Structural Topic Modeling Approach. International Journal of Environmental Research and Public Health 2022;19(19):12579
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  49. Du Y, Chen Z, Yang J, Morente-Molinera JA, Zhang L, Herrera-Viedma E. A Textual Data-Oriented Method for Doctor Selection in Online Health Communities. Sustainability 2023;15(2):1241
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  50. Wu J, Zhang G, Xing Y, Liu Y, Zhang Z, Dong Y, Herrera-Viedma E. A sentiment analysis driven method based on public and personal preferences with correlated attributes to select online doctors. Applied Intelligence 2023;53(16):19093
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  51. A. Rahim AI, Ibrahim MI, Musa KI, Chua S, Yaacob NM. Assessing Patient-Perceived Hospital Service Quality and Sentiment in Malaysian Public Hospitals Using Machine Learning and Facebook Reviews. International Journal of Environmental Research and Public Health 2021;18(18):9912
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  52. Xie Y, Xiang F. An improved approach based on dynamic mixed sampling and transfer learning for topic recognition: a case study on online patient reviews. Online Information Review 2022;46(6):1017
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  53. Barnett J, Bjarnadóttir MV, Anderson D, Chen C. Understanding Gender Biases and Differences in Web-Based Reviews of Sanctioned Physicians Through a Machine Learning Approach: Mixed Methods Study. JMIR Formative Research 2022;6(9):e34902
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  54. Chandrasekaran R, Bapat P, Jeripity Venkata P, Moustakas E. Do Patients Assess Physicians Differently in Video Visits as Compared with In-Person Visits? Insights from Text-Mining Online Physician Reviews. Telemedicine and e-Health 2023;29(10):1557
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  55. Shah AM, Muhammad W, Lee K, Naqvi RA. Examining Different Factors in Web-Based Patients’ Decision-Making Process: Systematic Review on Digital Platforms for Clinical Decision Support System. International Journal of Environmental Research and Public Health 2021;18(21):11226
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  56. Li G, Han C, Liu P. Does Internet Use Affect Medical Decisions among Older Adults in China? Evidence from CHARLS. Healthcare 2021;10(1):60
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  57. Wang H, Sun K, Wang Y. Exploring the Chinese Public’s Perception of Omicron Variants on Social Media: LDA-Based Topic Modeling and Sentiment Analysis. International Journal of Environmental Research and Public Health 2022;19(14):8377
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  58. Liu J, Gao L. Lurking or active? The influence of user participation behavior in online mental health communities on the choice and evaluation of doctors. Journal of Affective Disorders 2022;301:454
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  59. Khanbhai M, Warren L, Symons J, Flott K, Harrison-White S, Manton D, Darzi A, Mayer E. Using natural language processing to understand, facilitate and maintain continuity in patient experience across transitions of care. International Journal of Medical Informatics 2022;157:104642
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  60. Xu RH, Zhou L, Wong EL, Wang D. Investigating Medical Student's Preferences for Internet-Based Healthcare Services: A Best-Worst Scaling Survey. Frontiers in Public Health 2021;9
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  61. Fan W, Zhou Q, Qiu L, Kumar S. Should Doctors Open Online Consultation Services? An Empirical Investigation of Their Impact on Offline Appointments. Information Systems Research 2023;34(2):629
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  62. Shu T, Wang Z, Jia H, Zhao W, Zhou J, Peng T. Consumers’ Opinions towards Public Health Effects of Online Games: An Empirical Study Based on Social Media Comments in China. International Journal of Environmental Research and Public Health 2022;19(19):12793
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  63. Liu X, Zhou Y, Wang Z. Preference access of users' cancer risk perception using disease-specific online medical inquiry texts. Information Processing & Management 2022;59(1):102737
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  64. Tian R, Yin R, Gan F. Exploring public attitudes toward live-streaming fitness in China: A sentiment and content analysis of China's social media Weibo. Frontiers in Public Health 2022;10
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  65. Li J, Pang PC, Xiao Y, Wong D. Changes in Doctor–Patient Relationships in China during COVID-19: A Text Mining Analysis. International Journal of Environmental Research and Public Health 2022;19(20):13446
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  66. Zhou C, Yang S, Chen Y, Zhou S, Li Y, Qazi A. How does topic consistency affect online review helpfulness? The role of review emotional intensity. Electronic Commerce Research 2023;23(4):2943
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  67. Wang P, Li S, Wang Z, Jiao M, Zhang Y, Huang W, Ning N, Gao L, Shan L, Li Y, Wu Q. Perceptions of the benefits of the basic medical insurance system among the insured: a mixed methods research of a northern city in China. Frontiers in Public Health 2023;11
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  68. Yu H, Chiu Y, Wang J, Yu J, Hsu Y. Electronic consultation accessibility influence on patient assessments: A case–control study with user-generated tags of physician expertise. DIGITAL HEALTH 2023;9
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  69. Stanworth JO, Hsu RS, Stanworth PA, Kemp JM, Tzen R, Wu H. When Culture Matters: Using Compliments and Complaints to Define and Influence Chinese Patients’ Satisfaction. Health Communication 2024;39(1):136
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  70. Liu J, Jiang H. Exploring the Effects of Online Physician Voice Pitch Range and Filled Pauses on Patient Satisfaction in Mobile Health Communication. Health Communication 2024;:1
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  71. Kalabikhina I, Moshkin V, Kolotusha A, Kashin M, Klimenko G, Kazbekova Z. Advancing Semantic Classification: A Comprehensive Examination of Machine Learning Techniques in Analyzing Russian-Language Patient Reviews. Mathematics 2024;12(4):566
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  72. Liu X, Zhou Y, Wang Z, Kumar A, Biswas B. Disease Topic Modeling of Users' Inquiry Texts: A Text Mining-Based PQDR-LDA Model for Analyzing the Online Medical Records. IEEE Transactions on Engineering Management 2024;71:6319
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According to Crossref, the following books are citing this article (DOI 10.2196/jmir.4430):

  1. Shah AM, Yan X, Shah SJ, Khan S. Smart Health. 2018. Chapter 19:191
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  2. Kersting J, Geierhos M. Natural Language Processing in Artificial Intelligence—NLPinAI 2020. 2021. Chapter 6:163
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  3. Eti S, Mızrak F. Strategic Outlook for Innovative Work Behaviours. 2020. Chapter 2:21
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