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

mHealth for Data Collection and Research (955)

Published on in Vol 7, No 12 (2019): December

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/14473, first published .
Clustering Insomnia Patterns by Data From Wearable Devices: Algorithm Development and Validation Study

Clustering Insomnia Patterns by Data From Wearable Devices: Algorithm Development and Validation Study

Clustering Insomnia Patterns by Data From Wearable Devices: Algorithm Development and Validation Study

Authors of this article:

Sungkyu Park1 Author Orcid Image ;   Sang Won Lee2 Author Orcid Image ;   Sungwon Han3 Author Orcid Image ;   Meeyoung Cha3, 4 Author Orcid Image
Article Authors Cited by (16) Tweetations (12) Metrics

Journals

  1. Kim S. Recent Trends of Artificial Intelligence and Machine Learning for Insomnia Research. Chronobiology in Medicine 2021;3(1):16 View
  2. Sabry F, Eltaras T, Labda W, Alzoubi K, Malluhi Q, Wu Y. Machine Learning for Healthcare Wearable Devices: The Big Picture. Journal of Healthcare Engineering 2022;2022:1 View
  3. Lee W, Schwartz N, Bansal A, Khor S, Hammarlund N, Basu A, Devine B. A Scoping Review of the Use of Machine Learning in Health Economics and Outcomes Research: Part 1—Data From Wearable Devices. Value in Health 2023;26(2):292 View
  4. Bandyopadhyay A, Goldstein C. Clinical applications of artificial intelligence in sleep medicine: a sleep clinician’s perspective. Sleep and Breathing 2023;27(1):39 View
  5. Anmella G, Corponi F, Li B, Mas A, Sanabra M, Pacchiarotti I, Valentí M, Grande I, Benabarre A, Giménez-Palomo A, Garriga M, Agasi I, Bastidas A, Cavero M, Fernández-Plaza T, Arbelo N, Bioque M, García-Rizo C, Verdolini N, Madero S, Murru A, Amoretti S, Martínez-Aran A, Ruiz V, Fico G, De Prisco M, Oliva V, Solanes A, Radua J, Samalin L, Young A, Vieta E, Vergari A, Hidalgo-Mazzei D. Exploring Digital Biomarkers of Illness Activity in Mood Episodes: Hypotheses Generating and Model Development Study. JMIR mHealth and uHealth 2023;11:e45405 View
  6. Sedlakova J, Daniore P, Horn Wintsch A, Wolf M, Stanikic M, Haag C, Sieber C, Schneider G, Staub K, Alois Ettlin D, Grübner O, Rinaldi F, von Wyl V, Sarmiento R. Challenges and best practices for digital unstructured data enrichment in health research: A systematic narrative review. PLOS Digital Health 2023;2(10):e0000347 View
  7. Misra B, Roy N, Dey N, Sherratt R. Visualizing Wearable Medical Device Research Trends: A Co-occurrence Network-Based Bibliometric Analysis. Galician Medical Journal 2023;30(3):E202332 View
  8. Winger T, Chellamuthu V, Guzenko D, Aloia M, Barr S, DeFranco S, Gorski B, Mushtaq F, Garcia-Molina G. Fine tuned personalized machine learning models to detect insomnia risk based on data from a smart bed platform. Frontiers in Neurology 2024;15 View
  9. Amzil A, Abid M, Hanini M, Zaaloul A, El Kafhali S. Stochastic analysis of fog computing and machine learning for scalable low-latency healthcare monitoring. Cluster Computing 2024;27(5):6097 View
  10. Ingle M, Sharma M, Kumar K, Kumar P, Bhurane A, Elphick H, Joshi D, Acharya U. A systematic review on automatic identification of insomnia. Physiological Measurement 2024;45(3):03TR01 View
  11. Groninger H, Arem H, Ayangma L, Gong L, Zhou E, Greenberg D. Development of a voice-activated virtual assistant to improve insomnia among young adult cancer survivors: Focus groups findings and device feasibility testing (Preprint). JMIR Formative Research 2024 View

Books/Policy Documents

  1. Barrachina M, Valenzuela López L. Advancing the Investigation and Treatment of Sleep Disorders Using AI. View
  2. Sureja N, Mehta K, Shah V, Patel G. Machine Learning for Advanced Functional Materials. View
  3. Mujawar M, Salunke D, Mulani D, Gajare A, Deshmukh P, Ranjan N, Tekade P. Artificial Intelligence: Theory and Applications. View
  4. Zan X, Liu F, Xian X, Pardalos P. Handbook of AI and Data Sciences for Sleep Disorders. View