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

Fitness Trackers and Smart Pedometers/Accelerometers (571) New Methods (964) Machine Learning (1505) mHealth for Data Collection and Research (982)

Published on in Vol 8, No 7 (2020): July

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/16113, first published .
Deep Learning Approach for Imputation of Missing Values in Actigraphy Data: Algorithm Development Study

Deep Learning Approach for Imputation of Missing Values in Actigraphy Data: Algorithm Development Study

Deep Learning Approach for Imputation of Missing Values in Actigraphy Data: Algorithm Development Study

Authors of this article:

Jong-Hwan Jang1 Author Orcid Image ;   Junggu Choi1 Author Orcid Image ;   Hyun Woong Roh2 Author Orcid Image ;   Sang Joon Son3 Author Orcid Image ;   Chang Hyung Hong3 Author Orcid Image ;   Eun Young Kim1, 4 Author Orcid Image ;   Tae Young Kim1 Author Orcid Image ;   Dukyong Yoon1, 4 Author Orcid Image
Article Authors Cited by (19) Tweetations (2) Metrics

Journals

  1. Park B, Choi B, Lee H, Jang J, Roh H, Kim E, Hong C, Son S, Yoon D. Modeling Brain Volume Using Deep Learning-Based Physical Activity Features in Patients With Dementia. Frontiers in Neuroinformatics 2022;16 View
  2. Choi J, Ko I, Nah Y, Kim B, Park Y, Cha J, Choi J, Han S. Machine Learning Approach for Classifying College Scholastic Ability Test Levels With Unsupervised Features From Prefrontal Functional Near-Infrared Spectroscopy Signals. IEEE Access 2022;10:50864 View
  3. Li M, Huang Z, Shan Q, Chen S, Zhang N, Hu H, Wang W. Performance and comparison of artificial intelligence and human experts in the detection and classification of colonic polyps. BMC Gastroenterology 2022;22(1) View
  4. Xu Y, Liu X, Pan L, Mao X, Liang H, Wang G, Chen T. Explainable Dynamic Multimodal Variational Autoencoder for the Prediction of Patients With Suspected Central Precocious Puberty. IEEE Journal of Biomedical and Health Informatics 2022;26(3):1362 View
  5. Wrench D, Parashar T, Singh R, Frean M, Rayudu R. Exploring the Potential of Neural Networks to Predict Statistics of Solar Wind Turbulence. Space Weather 2022;20(9) View
  6. Chan P, Peskov K, Song X. Applications of Model-Based Meta-Analysis in Drug Development. Pharmaceutical Research 2022;39(8):1761 View
  7. Choi J, Nah Y, Ko I, Han S. Deep Learning Approach to Generate a Synthetic Cognitive Psychology Behavioral Dataset. IEEE Access 2021;9:142489 View
  8. Gao Z, Liu W, McDonough D, Zeng N, Lee J. The Dilemma of Analyzing Physical Activity and Sedentary Behavior with Wrist Accelerometer Data: Challenges and Opportunities. Journal of Clinical Medicine 2021;10(24):5951 View
  9. Weed L, Lok R, Chawra D, Zeitzer J. The Impact of Missing Data and Imputation Methods on the Analysis of 24-Hour Activity Patterns. Clocks & Sleep 2022;4(4):497 View
  10. Subramanian H. A Decentralized Marketplace for Patient-Generated Health Data: Design Science Approach. Journal of Medical Internet Research 2023;25:e42743 View
  11. Zhang Q, Wen J, Zhou J, Zhang B. Missing-view completion for fatty liver disease detection. Computers in Biology and Medicine 2022;150:106097 View
  12. Lee M, Hoang K, Park S, Song Y, Joo E, Chang W, Kim J, Kim J. Imputing missing sleep data from wearables with neural networks in real-world settings. SLEEP 2024;47(1) View
  13. Liu M, Li S, Yuan H, Ong M, Ning Y, Xie F, Saffari S, Shang Y, Volovici V, Chakraborty B, Liu N. Handling missing values in healthcare data: A systematic review of deep learning-based imputation techniques. Artificial Intelligence in Medicine 2023;142:102587 View
  14. Smith L, Oakden-Rayner L, Bird A, Zeng M, To M, Mukherjee S, Palmer L. Machine learning and deep learning predictive models for long-term prognosis in patients with chronic obstructive pulmonary disease: a systematic review and meta-analysis. The Lancet Digital Health 2023;5(12):e872 View
  15. Nugroho H, Surendro K. A Comprehensive Bibliometric Analysis of Missing Value Imputation. IEEE Access 2024;12:14819 View
  16. Tawakuli A, Havers B, Gulisano V, Kaiser D, Engel T. Survey:Time-series data preprocessing: A survey and an empirical analysis. Journal of Engineering Research 2024 View
  17. Firdaus F, Nurmaini S, Tutuko B, Rachmatullah M, Islami A, Darmawahyuni A, Sapitri A, Ais'sy W, Karim M, Fachrurrozi M, Zarkasi A. XU-NetI: Simple U-shaped encoder-decoder network for accurate imputation of multivariate missing data. Franklin Open 2024;8:100151 View
  18. Kang B, Ma J, Jeong I, Yoon S, Kim J, Heo S, Oh S. Behavioral marker-based predictive modeling of functional status for older adults with subjective cognitive decline and mild cognitive impairment: Study protocol. DIGITAL HEALTH 2024;10 View

Books/Policy Documents

  1. Isikhan S, Zararsiz G. Encyclopedia of Data Science and Machine Learning. View