Published on in Vol 20, No 11 (2018): November

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/12001, first published .
Defining and Predicting Pain Volatility in Users of the Manage My Pain App: Analysis Using Data Mining and Machine Learning Methods

Defining and Predicting Pain Volatility in Users of the Manage My Pain App: Analysis Using Data Mining and Machine Learning Methods

Defining and Predicting Pain Volatility in Users of the Manage My Pain App: Analysis Using Data Mining and Machine Learning Methods

Journals

  1. Burns J, Gerhart J, Rizvydeen M, Kimura M, Burgess H. Morning Bright Light Treatment for Chronic Low Back Pain: Potential Impact on the Volatility of Pain, Mood, Function, and Sleep. Pain Medicine 2020;21(6):1153 View
  2. Richardson P, Harrison L, Heathcote L, Rush G, Shear D, Lalloo C, Hood K, Wicksell R, Stinson J, Simons L. mHealth for pediatric chronic pain: state of the art and future directions. Expert Review of Neurotherapeutics 2020;20(11):1177 View
  3. Necka E, Lee I, Kucyi A, Cheng J, Yu Q, Atlas L. Applications of dynamic functional connectivity to pain and its modulation. PAIN Reports 2019;4(4):e752 View
  4. Moore R, Smith R, Liu Q. Using computational ethnography to enhance the curation of real-world data (RWD) for chronic pain and invisible disability use cases. ACM SIGACCESS Accessibility and Computing 2020;(127):1 View
  5. Simpao A, Rehman M. Anesthesia Informatics in 2018. Advances in Anesthesia 2019;37:145 View
  6. Rahman Q, Janmohamed T, Clarke H, Ritvo P, Heffernan J, Katz J. Interpretability and Class Imbalance in Prediction Models for Pain Volatility in Manage My Pain App Users: Analysis Using Feature Selection and Majority Voting Methods. JMIR Medical Informatics 2019;7(4):e15601 View
  7. Slepian P, Peng M, Janmohamed T, Kotteeswaran Y, Manoo V, Blades A, Fiorellino J, Katznelson R, Tamir D, McRae K, Kahn M, Huang A, Kona S, Thaker S, Weinrib A, Katz J, Clarke H. Engagement with Manage My Pain mobile health application among patients at the Transitional Pain Service. DIGITAL HEALTH 2020;6:205520762096229 View
  8. 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
  9. Jenssen M, Bakkevoll P, Ngo P, Budrionis A, Fagerlund A, Tayefi M, Bellika J, Godtliebsen F. Machine Learning in Chronic Pain Research: A Scoping Review. Applied Sciences 2021;11(7):3205 View
  10. Schwab J, Schobel J, Werle S, Furstberger A, Ikonomi N, Szekely R, Thiam P, Huhne R, Jahn N, Schuler R, Kuhn P, Holderried M, Steger F, Reichert M, Kaisers U, Kestler A, Seufferlein T, Kestler H. Perspective on mHealth Concepts to Ensure Users’ Empowerment–From Adverse Event Tracking for COVID-19 Vaccinations to Oncological Treatment. IEEE Access 2021;9:83863 View
  11. 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
  12. 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
  13. Su D, Li Q, Zhang T, Veliz P, Chen Y, He K, Mahajan P, Zhang X. Prediction of acute appendicitis among patients with undifferentiated abdominal pain at emergency department. BMC Medical Research Methodology 2022;22(1) View
  14. 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
  15. Matsangidou M, Liampas A, Pittara M, Pattichi C, Zis P. Machine Learning in Pain Medicine: An Up-To-Date Systematic Review. Pain and Therapy 2021;10(2):1067 View
  16. Zhang P, Jiang Y, Liu G, Han J, Wang J, Ma L, Hu W, Zhang J. Altered brain functional network dynamics in classic trigeminal neuralgia: a resting-state functional magnetic resonance imaging study. The Journal of Headache and Pain 2021;22(1) View
  17. Xu G, Tang W, Zhou C, Xu J, Cheng C, Gong W, Dong S, Zhang Y. Pain Fluctuations of Women with Subacute Herpetic Neuralgia During Local Methylcobalamin in Combination with Lidocaine Treatment: A Single-Blinded Randomized Controlled Trial. Journal of Pain Research 2023;Volume 16:1267 View
  18. Koh R, Khan M, Rashidiani S, Hassan S, Tucci V, Liu T, Nesovic K, Kumbhare D, Doyle T. Check It Before You Wreck It: A Guide to STAR-ML for Screening Machine Learning Reporting in Research. IEEE Access 2023;11:101567 View
  19. Qian Y, Alhaskawi A, Dong Y, Ni J, Abdalbary S, Lu H. Transforming medicine: artificial intelligence integration in the peripheral nervous system. Frontiers in Neurology 2024;15 View
  20. 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

Books/Policy Documents

  1. Cho P, Singh K, Dunn J. Artificial Intelligence in Medicine. View
  2. Xia M. Anesthesia for Oral and Maxillofacial Surgery. View
  3. Verma D, Bach K, Mork P. Artificial Intelligence XXXVIII. View