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Citing this Article

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Published on 15.03.17 in Vol 19, No 3 (2017): March

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

Works citing "Supervised Machine Learning Algorithms Can Classify Open-Text Feedback of Doctor Performance With Human-Level Accuracy"

Chris Gibbons, Suzanne Richards, Jose Maria Valderas, John Campbell

J Med Internet Res 2017 (Mar 15); 19(3):e65 

According to Crossref, the following articles are citing this article (DOI 10.2196/jmir.6533):

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

  1. Delespierre T, Josseran L. Issues in Building a Nursing Home Syndromic Surveillance System with Textmining: Longitudinal Observational Study. JMIR Public Health and Surveillance 2018;4(4):e69
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  2. Liu JB, Pusic AL, Gibbons CJ, Opelka FG, Sage JS, Thompson VM, Ko CY, Hall BL, Temple LK. Association of Patient-reported Experiences and Surgical Outcomes Among Group Practices. Annals of Surgery 2020;271(3):475
    CrossRef
  3. Rivas C, Tkacz D, Antao L, Mentzakis E, Gordon M, Anstee S, Giordano R. Automated analysis of free-text comments and dashboard representations in patient experience surveys: a multimethod co-design study. Health Services and Delivery Research 2019;7(23):1
    CrossRef
  4. Sanders C, Nahar P, Small N, Hodgson D, Ong BN, Dehghan A, Sharp CA, Dixon WG, Lewis S, Kontopantelis E, Daker-White G, Bower P, Davies L, Kayesh H, Spencer R, McAvoy A, Boaden R, Lovell K, Ainsworth J, Nowakowska M, Shepherd A, Cahoon P, Hopkins R, Allen D, Lewis A, Nenadic G. Digital methods to enhance the usefulness of patient experience data in services for long-term conditions: the DEPEND mixed-methods study. Health Services and Delivery Research 2020;8(28):1
    CrossRef
  5. Kabir MF, Ludwig SA. Enhancing the Performance of Classification Using Super Learning. Data-Enabled Discovery and Applications 2019;3(1)
    CrossRef
  6. Garcia JR, Stout CT. Responding to Racial Resentment: How Racial Resentment Influences Legislative Behavior. Political Research Quarterly 2020;73(4):805
    CrossRef
  7. . Framing the Taxation-Democratization Link: An Automated Content Analysis of Cross-National Newspaper Data. The International Journal of Press/Politics 2018;23(2):247
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  8. Park Y, Bae JH, Shin MH, Hyun SH, Cho YS, Choe YS, Choi JY, Lee K, Kim B, Moon SH. Development of Predictive Models in Patients with Epiphora Using Lacrimal Scintigraphy and Machine Learning. Nuclear Medicine and Molecular Imaging 2019;53(2):125
    CrossRef
  9. Gibbons C, Greaves F. Lending a hand: could machine learning help hospital staff make better use of patient feedback?. BMJ Quality & Safety 2018;27(2):93
    CrossRef
  10. Williams L, Trussardi G, Black S, Moeke-Maxwell T, Frey R, Robinson J, Gott M. Complex contradictions in conceptualisations of ‘dignity’ in palliative care. International Journal of Palliative Nursing 2018;24(1):12
    CrossRef
  11. Shah RF, Bini SA, Martinez AM, Pedoia V, Vail TP. Incremental inputs improve the automated detection of implant loosening using machine-learning algorithms. The Bone & Joint Journal 2020;102-B(6_Supple_A):101
    CrossRef
  12. Abraham TH, Deen TL, Hamilton M, True G, O’Neil MT, Blanchard J, Uddo M. Analyzing free-text survey responses: An accessible strategy for developing patient-centered programs and program evaluation. Evaluation and Program Planning 2020;78:101733
    CrossRef
  13. Liu JB, Pusic AL, Matroniano A, Aryal R, Willarson PB, Hall BL, Temple LK, Ko CY. First Report of a Multiphase Pilot to Measure Patient-Reported Outcomes in the American College of Surgeons National Surgical Quality Improvement Program. The Joint Commission Journal on Quality and Patient Safety 2019;45(5):319
    CrossRef
  14. Harrison C, Loe BS, Lis P, Sidey-Gibbons C. Maximizing the Potential of Patient-Reported Assessments by Using the Open-Source Concerto Platform With Computerized Adaptive Testing and Machine Learning. Journal of Medical Internet Research 2020;22(10):e20950
    CrossRef
  15. Sidey-Gibbons JAM, Sidey-Gibbons CJ. Machine learning in medicine: a practical introduction. BMC Medical Research Methodology 2019;19(1)
    CrossRef
  16. Dias RD, Gupta A, Yule SJ. Using Machine Learning to Assess Physician Competence: A Systematic Review. Academic Medicine 2019;94(3):427
    CrossRef
  17. Menendez ME, Shaker J, Lawler SM, Ring D, Jawa A. Negative Patient-Experience Comments After Total Shoulder Arthroplasty. Journal of Bone and Joint Surgery 2019;101(4):330
    CrossRef
  18. Duan T, Rajpurkar P, Laird D, Ng AY, Basu S. Clinical Value of Predicting Individual Treatment Effects for Intensive Blood Pressure Therapy. Circulation: Cardiovascular Quality and Outcomes 2019;12(3)
    CrossRef
  19. Shen J, Zhang CJP, Jiang B, Chen J, Song J, Liu Z, He Z, Wong SY, Fang P, Ming W. Artificial Intelligence Versus Clinicians in Disease Diagnosis: Systematic Review. JMIR Medical Informatics 2019;7(3):e10010
    CrossRef
  20. . A survey on data‐efficient algorithms in big data era. Journal of Big Data 2021;8(1)
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  21. Tolsgaard MG, Boscardin CK, Park YS, Cuddy MM, Sebok-Syer SS. The role of data science and machine learning in Health Professions Education: practical applications, theoretical contributions, and epistemic beliefs. Advances in Health Sciences Education 2020;25(5):1057
    CrossRef
  22. 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
    CrossRef
  23. Erkinay Ozdemir M, Ali Z, Subeshan B, Asmatulu E. Applying machine learning approach in recycling. Journal of Material Cycles and Waste Management 2021;23(3):855
    CrossRef
  24. Rahim MA, Hassan HM. A deep learning based traffic crash severity prediction framework. Accident Analysis & Prevention 2021;154:106090
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  25. Donnellan E, Aslan S, Fastrich GM, Murayama K. How Are Curiosity and Interest Different? Naïve Bayes Classification of People’s Beliefs. Educational Psychology Review 2022;34(1):73
    CrossRef
  26. Moonen-van Loon JMW, Govaerts M, Donkers J, van Rosmalen P. Toward Automatic Interpretation of Narrative Feedback in Competency-Based Portfolios. IEEE Transactions on Learning Technologies 2022;15(2):179
    CrossRef
  27. Niyogisubizo J, Liao L, Sun Q, Nziyumva E, Wang Y, Luo L, Lai S, Murwanashyaka E. Predicting Crash Injury Severity in Smart Cities: a Novel Computational Approach with Wide and Deep Learning Model. International Journal of Intelligent Transportation Systems Research 2023;21(1):240
    CrossRef
  28. Grechishcheva S, Lenivtceva I, Kopanitsa G, Panfilov D. Filtering free-text medical data based on machine learning. Procedia Computer Science 2021;193:82
    CrossRef
  29. Small N, Ong BN, Lewis A, Allen D, Bagshaw N, Nahar P, Sanders C, Hodgson D, Dehghan A, Sharp C, Dixon W, Lewis S, Kontopantelis E, Daker-White G, Bower P, Davies L, Kayesh H, Spencer R, McAvoy A, Boaden R, Lovell K, Ainsworth J, Nowakowska M, Shepherd A, Cahoon P, Hopkins R, Nenadic G. Co-designing new tools for collecting, analysing and presenting patient experience data in NHS services: working in partnership with patients and carers. Research Involvement and Engagement 2021;7(1)
    CrossRef
  30. 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
    CrossRef
  31. Hudon A, Phraxayavong K, Potvin S, Dumais A. Comparing the Performance of Machine Learning Algorithms in the Automatic Classification of Psychotherapeutic Interactions in Avatar Therapy. Machine Learning and Knowledge Extraction 2023;5(3):1119
    CrossRef
  32. Xu C, Pfob A, Mehrara BJ, Yin P, Nelson JA, Pusic AL, Sidey-Gibbons C. Enhanced Surgical Decision-Making Tools in Breast Cancer: Predicting 2-Year Postoperative Physical, Sexual, and Psychosocial Well-Being following Mastectomy and Breast Reconstruction (INSPiRED 004). Annals of Surgical Oncology 2023;30(12):7046
    CrossRef
  33. Gordon M, Daniel M, Ajiboye A, Uraiby H, Xu NY, Bartlett R, Hanson J, Haas M, Spadafore M, Grafton-Clarke C, Gasiea RY, Michie C, Corral J, Kwan B, Dolmans D, Thammasitboon S. A scoping review of artificial intelligence in medical education: BEME Guide No. 84. Medical Teacher 2024;46(4):446
    CrossRef
  34. Miladinia M, Zarea K, Gheibizadeh M, Jahangiri M, Karimpourian H, Rokhafroz D. A multiphase study protocol of identifying, and predicting cancer-related symptom clusters: applying a mixed-method design and machine learning algorithms. Frontiers in Digital Health 2024;6
    CrossRef

According to Crossref, the following books are citing this article (DOI 10.2196/jmir.6533):

  1. Bhardwaj T, Somvanshi P. Machine Intelligence and Signal Analysis. 2019. Chapter 58:683
    CrossRef
  2. de Lima ALI, de Sousa Lima RJ, da Hora HRM. Advances in Multidisciplinary Medical Technologies ─ Engineering, Modeling and Findings. 2021. Chapter 2:11
    CrossRef
  3. Kabir MF, Ludwig SA. Applied Smart Health Care Informatics. 2022. :111
    CrossRef