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

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

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

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

Journals

  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 View
  2. Liu J, Pusic A, Gibbons C, Opelka F, Sage J, Thompson V, Ko C, Hall B, Temple L. Association of Patient-reported Experiences and Surgical Outcomes Among Group Practices. Annals of Surgery 2020;271(3):475 View
  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 View
  4. Sanders C, Nahar P, Small N, Hodgson D, Ong B, 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, 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 View
  5. Kabir M, Ludwig S. Enhancing the Performance of Classification Using Super Learning. Data-Enabled Discovery and Applications 2019;3(1) View
  6. Garcia J, Stout C. Responding to Racial Resentment: How Racial Resentment Influences Legislative Behavior. Political Research Quarterly 2020;73(4):805 View
  7. Kananovich V. Framing the Taxation-Democratization Link: An Automated Content Analysis of Cross-National Newspaper Data. The International Journal of Press/Politics 2018;23(2):247 View
  8. Park Y, Bae J, Shin M, Hyun S, Cho Y, Choe Y, Choi J, Lee K, Kim B, Moon S. Development of Predictive Models in Patients with Epiphora Using Lacrimal Scintigraphy and Machine Learning. Nuclear Medicine and Molecular Imaging 2019;53(2):125 View
  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 View
  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 View
  11. Shah R, Bini S, Martinez A, Pedoia V, Vail T. Incremental inputs improve the automated detection of implant loosening using machine-learning algorithms. The Bone & Joint Journal 2020;102-B(6_Supple_A):101 View
  12. Abraham T, Deen T, Hamilton M, True G, O’Neil M, 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 View
  13. Liu J, Pusic A, Matroniano A, Aryal R, Willarson P, Hall B, Temple L, Ko C. 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 View
  14. Harrison C, Loe B, 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 View
  15. Sidey-Gibbons J, Sidey-Gibbons C. Machine learning in medicine: a practical introduction. BMC Medical Research Methodology 2019;19(1) View
  16. Dias R, Gupta A, Yule S. Using Machine Learning to Assess Physician Competence: A Systematic Review. Academic Medicine 2019;94(3):427 View
  17. Menendez M, Shaker J, Lawler S, Ring D, Jawa A. Negative Patient-Experience Comments After Total Shoulder Arthroplasty. Journal of Bone and Joint Surgery 2019;101(4):330 View
  18. Duan T, Rajpurkar P, Laird D, Ng A, Basu S. Clinical Value of Predicting Individual Treatment Effects for Intensive Blood Pressure Therapy. Circulation: Cardiovascular Quality and Outcomes 2019;12(3) View
  19. Shen J, Zhang C, Jiang B, Chen J, Song J, Liu Z, He Z, Wong S, Fang P, Ming W. Artificial Intelligence Versus Clinicians in Disease Diagnosis: Systematic Review. JMIR Medical Informatics 2019;7(3):e10010 View
  20. Adadi A. A survey on data‐efficient algorithms in big data era. Journal of Big Data 2021;8(1) View
  21. Tolsgaard M, Boscardin C, Park Y, Cuddy M, Sebok-Syer S. 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 View
  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 View
  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 View
  24. Rahim M, Hassan H. A deep learning based traffic crash severity prediction framework. Accident Analysis & Prevention 2021;154:106090 View
  25. Donnellan E, Aslan S, Fastrich G, Murayama K. How Are Curiosity and Interest Different? Naïve Bayes Classification of People’s Beliefs. Educational Psychology Review 2022;34(1):73 View
  26. Moonen-van Loon J, 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 View
  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 View
  28. Grechishcheva S, Lenivtceva I, Kopanitsa G, Panfilov D. Filtering free-text medical data based on machine learning. Procedia Computer Science 2021;193:82 View
  29. Small N, Ong B, 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) View
  30. Li J, Pang P, 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 View
  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 View
  32. Xu C, Pfob A, Mehrara B, Yin P, Nelson J, Pusic A, 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 View
  33. Gordon M, Daniel M, Ajiboye A, Uraiby H, Xu N, Bartlett R, Hanson J, Haas M, Spadafore M, Grafton-Clarke C, Gasiea R, 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 View
  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 View
  35. Burt J, Newbould J, Abel G, Elliott M, Beckwith J, Llanwarne N, Elmore N, Davey A, Gibbons C, Campbell J, Roland M. Investigating the meaning of ‘good’ or ‘very good’ patient evaluations of care in English general practice: a mixed methods study. BMJ Open 2017;7(3):e014718 View
  36. Liu J, Kaplan R, Bates D, Edelen M, Sisodia R, Pusic A. Mass General Brigham’s Patient-Reported Outcomes Measurement System: A Decade of Learnings. NEJM Catalyst 2024;5(7) View
  37. Nair B, Moonen - van Loon J, van Lierop M, Govaerts M. Leveraging Narrative Feedback in Programmatic Assessment: The Potential of Automated Text Analysis to Support Coaching and Decision-Making in Programmatic Assessment. Advances in Medical Education and Practice 2024;Volume 15:671 View

Books/Policy Documents

  1. Bhardwaj T, Somvanshi P. Machine Intelligence and Signal Analysis. View
  2. de Lima A, de Sousa Lima R, da Hora H. Advances in Multidisciplinary Medical Technologies ─ Engineering, Modeling and Findings. View
  3. Kabir M, Ludwig S. Applied Smart Health Care Informatics. View