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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. 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
  2. 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
  3. 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 2018;
    CrossRef
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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
  9. 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
  10. Kim I, Oh JM. Deep learning: from chemoinformatics to precision medicine. Journal of Pharmaceutical Investigation 2017;47(4):317
    CrossRef
  11. 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
  12. 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

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

:
  1. Ozogur G, Erturk MA, Aydin MA. International Telecommunications Conference. 2019. Chapter 26:299
    CrossRef