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Citing this Article

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Published on 04.11.16 in Vol 4, No 4 (2016): Oct-Dec

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

Works citing "Sleep Quality Prediction From Wearable Data Using Deep Learning"

According to Crossref, the following articles are citing this article (DOI 10.2196/mhealth.6562):

(note that this is only a small subset of citations)

  1. Muhammed T, Mehmood R, Albeshri A, Katib I. UbeHealth: A Personalized Ubiquitous Cloud and Edge-Enabled Networked Healthcare System for Smart Cities. IEEE Access 2018;6:32258
    CrossRef
  2. Kim I, Oh JM. Deep learning: from chemoinformatics to precision medicine. Journal of Pharmaceutical Investigation 2017;47(4):317
    CrossRef
  3. Obinikpo A, Kantarci B. Big Sensed Data Meets Deep Learning for Smarter Health Care in Smart Cities. Journal of Sensor and Actuator Networks 2017;6(4):26
    CrossRef
  4. Karampela M, Isomursu M, Porat T, Maramis C, Mountford N, Giunti G, Chouvarda I, Lehocki F. The Extent and Coverage of Current Knowledge of Connected Health: Systematic Mapping Study. Journal of Medical Internet Research 2019;21(9):e14394
    CrossRef
  5. Lee Y, Chou W, Chien T, Chou P, Yeh Y, Lee H. An App Developed for Detecting Nurse Burnouts Using the Convolutional Neural Networks in Microsoft Excel: Population-Based Questionnaire Study. JMIR Medical Informatics 2020;8(5):e16528
    CrossRef
  6. Liu X, Sun B, Zhang Z, Wang Y, Tang H, Zhu T, Ostadabbas S. Gait can reveal sleep quality with machine learning models. PLOS ONE 2019;14(9):e0223012
    CrossRef
  7. Fellger A, Sprint G, Weeks D, Crooks E, Cook DJ. Wearable Device-Independent Next Day Activity and Next Night Sleep Prediction for Rehabilitation Populations. IEEE Journal of Translational Engineering in Health and Medicine 2020;8:1
    CrossRef
  8. Chae D, Shin JA, Kim S. Collaborative Adversarial Autoencoders: An Effective Collaborative Filtering Model Under the GAN Framework. IEEE Access 2019;7:37650
    CrossRef
  9. Karpov YL, Karpov LE, Smetanin YG. Adaptation of General Concepts of Software Testing to Neural Networks. Programming and Computer Software 2018;44(5):324
    CrossRef
  10. Rönkkö K. An Activity Tracker and Its Accompanying App as a Motivator for Increased Exercise and Better Sleeping Habits for Youths in Need of Social Care: Field Study. JMIR mHealth and uHealth 2018;6(12):e193
    CrossRef
  11. Johnston W, Heiderscheit B. Mobile Technology in Running Science and Medicine: Are We Ready?. Journal of Orthopaedic & Sports Physical Therapy 2019;49(3):122
    CrossRef
  12. Xu H, Li P, Yang Z, Liu X, Wang Z, Yan W, He M, Chu W, She Y, Li Y, Cao D, Yan M, Zhang Z. Construction and Application of a Medical-Grade Wireless Monitoring System for Physiological Signals at General Wards. Journal of Medical Systems 2020;44(10)
    CrossRef
  13. Yan Y, Chien T, Yeh Y, Chou W, Hsing S. An App for Classifying Personal Mental Illness at Workplace Using Fit Statistics and Convolutional Neural Networks: Survey-Based Quantitative Study. JMIR mHealth and uHealth 2020;8(7):e17857
    CrossRef
  14. Arora A, Chakraborty P, Bhatia MPS. Analysis of Data from Wearable Sensors for Sleep Quality Estimation and Prediction Using Deep Learning. Arabian Journal for Science and Engineering 2020;
    CrossRef
  15. Mendonca F, Mostafa SS, Morgado-Dias F, Julia-Serda G, Ravelo-Garcia AG. A Method for Sleep Quality Analysis Based on CNN Ensemble With Implementation in a Portable Wireless Device. IEEE Access 2020;8:158523
    CrossRef
  16. Jiang S, Chin K, Tsui KL. A universal deep learning approach for modeling the flow of patients under different severities. Computer Methods and Programs in Biomedicine 2018;154:191
    CrossRef
  17. Dorraki M, Fouladzadeh A, Salamon SJ, Allison A, Coventry BJ, Abbott D. Can C-Reactive Protein (CRP) Time Series Forecasting be Achieved via Deep Learning?. IEEE Access 2019;7:59311
    CrossRef
  18. Thomas-Brown PL, Martin JS, Sewell CA, Abel WD, Gossell-Williams MD. Risperidone Provides Better Improvement of Sleep Disturbances Than Haloperidol Therapy in Schizophrenia Patients With Cannabis-Positive Urinalysis. Frontiers in Pharmacology 2018;9
    CrossRef
  19. Gorini A, Mazzocco K, Triberti S, Sebri V, Savioni L, Pravettoni G. A P5 Approach to m-Health: Design Suggestions for Advanced Mobile Health Technology. Frontiers in Psychology 2018;9
    CrossRef
  20. Zhang H, Deng K, Li H, Albin RL, Guan Y. Deep Learning Identifies Digital Biomarkers for Self-Reported Parkinson's Disease. Patterns 2020;1(3):100042
    CrossRef
  21. Koppe G, Meyer-Lindenberg A, Durstewitz D. Deep learning for small and big data in psychiatry. Neuropsychopharmacology 2020;
    CrossRef
  22. Park K, Lee S, Lee S, Cho S, Wang S, Kim S, Lee E. Sleep prediction algorithm based on machine learning technology. European Neuropsychopharmacology 2019;29:S514
    CrossRef
  23. Ma S, Chou W, Chien T, Chow JC, Yeh Y, Chou P, Lee H. An App for Detecting Bullying of Nurses Using Convolutional Neural Networks and Web-Based Computerized Adaptive Testing: Development and Usability Study. JMIR mHealth and uHealth 2020;8(5):e16747
    CrossRef
  24. Arabacı MA, Özkan F, Surer E, Jančovič P, Temizel A. Multi-modal egocentric activity recognition using multi-kernel learning. Multimedia Tools and Applications 2020;
    CrossRef
  25. Fallmann S, Chen L. Computational Sleep Behavior Analysis: A Survey. IEEE Access 2019;7:142421
    CrossRef
  26. M. Al-Eidan R, Al-Khalifa H, Al-Salman A. Deep-Learning-Based Models for Pain Recognition: A Systematic Review. Applied Sciences 2020;10(17):5984
    CrossRef
  27. Farajtabar M, Kıcıman E, Nathan G, White RW. Modeling behaviors and lifestyle with online and social data for predicting and analyzing sleep and exercise quality. International Journal of Data Science and Analytics 2019;8(4):367
    CrossRef
  28. Sathyanarayana A, Srivastava J, Fernandez-Luque L. The Science of Sweet Dreams: Predicting Sleep Efficiency from Wearable Device Data. Computer 2017;50(3):30
    CrossRef
  29. Sadeghi R, Banerjee T, Hughes JC, Lawhorne LW. Sleep quality prediction in caregivers using physiological signals. Computers in Biology and Medicine 2019;110:276
    CrossRef
  30. Chu H, Liu Y, Kuo F. A Mobile Sleep-Management Learning System for Improving Students’ Sleeping Habits by Integrating a Self-Regulated Learning Strategy: Randomized Controlled Trial. JMIR mHealth and uHealth 2018;6(10):e11557
    CrossRef
  31. Cho T, Sunarya U, Yeo M, Hwang B, Koo YS, Park C. Deep-ACTINet: End-to-End Deep Learning Architecture for Automatic Sleep-Wake Detection Using Wrist Actigraphy. Electronics 2019;8(12):1461
    CrossRef
  32. Berrouiguet S, Ramírez D, Barrigón ML, Moreno-Muñoz P, Carmona Camacho R, Baca-García E, Artés-Rodríguez A. Combining Continuous Smartphone Native Sensors Data Capture and Unsupervised Data Mining Techniques for Behavioral Changes Detection: A Case Series of the Evidence-Based Behavior (eB2) Study. JMIR mHealth and uHealth 2018;6(12):e197
    CrossRef
  33. Durstewitz D, Koppe G, Meyer-Lindenberg A. Deep neural networks in psychiatry. Molecular Psychiatry 2019;24(11):1583
    CrossRef
  34. Bing D, Ying J, Miao J, Lan L, Wang D, Zhao L, Yin Z, Yu L, Guan J, Wang Q. Predicting the hearing outcome in sudden sensorineural hearing loss via machine learning models. Clinical Otolaryngology 2018;43(3):868
    CrossRef
  35. Kim H, Lee S, Lee S, Hong S, Kang H, Kim N. Depression Prediction by Using Ecological Momentary Assessment, Actiwatch Data, and Machine Learning: Observational Study on Older Adults Living Alone. JMIR mHealth and uHealth 2019;7(10):e14149
    CrossRef

According to Crossref, the following books are citing this article (DOI 10.2196/mhealth.6562):

  1. Dincelli E, Zhou X, Yayla A, Jafarian H. Privacy Concerns Surrounding Personal Information Sharing on Health and Fitness Mobile Apps. 2021. chapter 4:83
    CrossRef
  2. Ebert DD, Harrer M, Apolinário-Hagen J, Baumeister H. Frontiers in Psychiatry. 2019. Chapter 29:583
    CrossRef
  3. Ignatov A, Timofte R, Chou W, Wang K, Wu M, Hartley T, Van Gool L. Computer Vision – ECCV 2018 Workshops. 2019. Chapter 19:288
    CrossRef
  4. El-Gayar OF, Ambati LS, Nawar N. AI and Big Data’s Potential for Disruptive Innovation. 2020. chapter 5:104
    CrossRef
  5. Ozogur G, Erturk MA, Aydin MA. International Telecommunications Conference. 2019. Chapter 26:299
    CrossRef
  6. Turner JT, Floyd MW, Gupta K, Oates T. Case-Based Reasoning Research and Development. 2019. Chapter 25:373
    CrossRef