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. 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 2021 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