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

Machine Learning (1501) Mobile Health (mhealth) (2982) mHealth for Symptom and Disease Monitoring, Chronic Disease Management (1428)

Published on in Vol 7, No 4 (2019): Oct-Dec

Interpretability and Class Imbalance in Prediction Models for Pain Volatility in Manage My Pain App Users: Analysis Using Feature Selection and Majority Voting Methods

Interpretability and Class Imbalance in Prediction Models for Pain Volatility in Manage My Pain App Users: Analysis Using Feature Selection and Majority Voting Methods

Interpretability and Class Imbalance in Prediction Models for Pain Volatility in Manage My Pain App Users: Analysis Using Feature Selection and Majority Voting Methods

Authors of this article:

Quazi Abidur Rahman1, 2 Author Orcid Image ;   Tahir Janmohamed3 Author Orcid Image ;   Hance Clarke4 Author Orcid Image ;   Paul Ritvo5 Author Orcid Image ;   Jane Heffernan1 Author Orcid Image ;   Joel Katz4, 5, 6 Author Orcid Image
Article Authors Cited by (11) Tweetations (7) Metrics

Journals

  1. Zhou J, Lu J, Gao C, Zeng J, Zhou C, Lai X, Cai W, Xu M. Predicting the response to neoadjuvant chemotherapy for breast cancer: wavelet transforming radiomics in MRI. BMC Cancer 2020;20(1) View
  2. Bhatia A, Kara J, Janmohamed T, Prabhu A, Lebovic G, Katz J, Clarke H. User Engagement and Clinical Impact of the Manage My Pain App in Patients With Chronic Pain: A Real-World, Multi-site Trial. JMIR mHealth and uHealth 2021;9(3):e26528 View
  3. Xia M, Xu T, Jiang H. Progress and Perspective of Artificial Intelligence and Machine Learning of Prediction in Anesthesiology. Journal of Shanghai Jiaotong University (Science) 2022;27(1):112 View
  4. Jia L, Wei Z, Zhang H, Wang J, Jia R, Zhou M, Li X, Zhang H, Chen X, Yu Z, Wang Z, Li X, Li T, Liu X, Liu P, Chen W, Li J, He K. An interpretable machine learning model based on a quick pre-screening system enables accurate deterioration risk prediction for COVID-19. Scientific Reports 2021;11(1) View
  5. Gerner M, Vuillerme N, Aubourg T, Messner E, Terhorst Y, Hörmann V, Ganzleben I, Schenker H, Schett G, Atreya R, Neurath M, Knitza J, Orlemann T. Review and Analysis of German Mobile Apps for Inflammatory Bowel Disease Management Using the Mobile Application Rating Scale: Systematic Search in App Stores and Content Analysis. JMIR mHealth and uHealth 2022;10(5):e31102 View
  6. Verma D, Bach K, Mork P. Application of Machine Learning Methods on Patient Reported Outcome Measurements for Predicting Outcomes: A Literature Review. Informatics 2021;8(3):56 View
  7. Verma D, Jansen D, Bach K, Poel M, Mork P, d’Hollosy W. Exploratory application of machine learning methods on patient reported data in the development of supervised models for predicting outcomes. BMC Medical Informatics and Decision Making 2022;22(1) View
  8. Zhao S, Ju Y, Ye X, Zhang J, Han S. Bioluminescent Proteins Prediction with Voting Strategy. Current Bioinformatics 2021;16(2):240 View
  9. Khan M, Koh R, Rashidiani S, Liu T, Tucci V, Kumbhare D, Doyle T. Cracking the Chronic Pain code: A scoping review of Artificial Intelligence in Chronic Pain research. Artificial Intelligence in Medicine 2024;151:102849 View
  10. Antel R, Whitelaw S, Gore G, Ingelmo P. Moving towards the use of artificial intelligence in pain management. European Journal of Pain 2024 View

Books/Policy Documents

  1. Verma D, Bach K, Mork P. Artificial Intelligence XXXVIII. View