Published on in Vol 7, No 8 (2019): August

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/11966, first published .
Deep Learning Intervention for Health Care Challenges: Some Biomedical Domain Considerations

Deep Learning Intervention for Health Care Challenges: Some Biomedical Domain Considerations

Deep Learning Intervention for Health Care Challenges: Some Biomedical Domain Considerations

Journals

  1. Uddin M, Shah S, Al-Khasawneh M. A Novel Deep Convolutional Neural Network Model to Monitor People following Guidelines to Avoid COVID-19. Journal of Sensors 2020;2020:1 View
  2. Ellis R, Ellestad E, Elicker B, Hope M, Tosun D. Impact of hybrid supervision approaches on the performance of artificial intelligence for the classification of chest radiographs. Computers in Biology and Medicine 2020;120:103699 View
  3. Adans-Dester C, Bamberg S, Bertacchi F, Caulfield B, Chappie K, Demarchi D, Erb M, Estrada J, Fabara E, Freni M, Friedl K, Ghaffari R, Gill G, Greenberg M, Hoyt R, Jovanov E, Kanzler C, Katabi D, Kernan M, Kigin C, Lee S, Leonhardt S, Lovell N, Mantilla J, McCoy T, Luo N, Miller G, Moore J, O'Keeffe D, Palmer J, Parisi F, Patel S, Po J, Pugliese B, Quatieri T, Rahman T, Ramasarma N, Rogers J, Ruiz-Esparza G, Sapienza S, Schiurring G, Schwamm L, Shafiee H, Kelly Silacci S, Sims N, Talkar T, Tharion W, Toombs J, Uschnig C, Vergara-Diaz G, Wacnik P, Wang M, Welch J, Williamson L, Zafonte R, Zai A, Zhang Y, Tearney G, Ahmad R, Walt D, Bonato P. Can mHealth Technology Help Mitigate the Effects of the COVID-19 Pandemic?. IEEE Open Journal of Engineering in Medicine and Biology 2020;1:243 View
  4. Sufian A, Ghosh A, Sadiq A, Smarandache F. A Survey on Deep Transfer Learning to Edge Computing for Mitigating the COVID-19 Pandemic. Journal of Systems Architecture 2020;108:101830 View
  5. Choi Y, Kim Y, Chung J, Kim K, Kim H, Park R, Park D. Effect of Age on the Initiation of Biologic Agent Therapy in Patients With Inflammatory Bowel Disease: Korean Common Data Model Cohort Study. JMIR Medical Informatics 2020;8(4):e15124 View
  6. Ma S, Chou W, Chien T, Chow J, 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 View
  7. Giacobbe D, Mora S, Giacomini M, Bassetti M. Machine Learning and Multidrug-Resistant Gram-Negative Bacteria: An Interesting Combination for Current and Future Research. Antibiotics 2020;9(2):54 View
  8. 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 View
  9. Aerts J. Special Issue on “Human Health Engineering”. Applied Sciences 2020;10(2):564 View
  10. Rim B, Sung N, Min S, Hong M. Deep Learning in Physiological Signal Data: A Survey. Sensors 2020;20(4):969 View
  11. Farsi M. Application of ensemble RNN deep neural network to the fall detection through IoT environment. Alexandria Engineering Journal 2020 View
  12. Kelly J, Campbell K, Gong E, Scuffham P. The Internet of Things: Impact and Implications for Health Care Delivery. Journal of Medical Internet Research 2020;22(11):e20135 View
  13. Urda D, Veredas F, González-Enrique J, Ruiz-Aguilar J, Jerez J, Turias I. Deep neural networks architecture driven by problem-specific information. Neural Computing and Applications 2021;33(15):9403 View
  14. Kaliyapillai S, Krishnamurthy S. Differential Evolution based Hyperparameters Tuned Deep Learning Models for Disease Diagnosis and Classification. Advances in Science, Technology and Engineering Systems Journal 2020;5(5):253 View
  15. Giacobbe D, Signori A, Del Puente F, Mora S, Carmisciano L, Briano F, Vena A, Ball L, Robba C, Pelosi P, Giacomini M, Bassetti M. Early Detection of Sepsis With Machine Learning Techniques: A Brief Clinical Perspective. Frontiers in Medicine 2021;8 View
  16. Kumar S, Sharma S. Sub-classification of invasive and non-invasive cancer from magnification independent histopathological images using hybrid neural networks. Evolutionary Intelligence 2021 View
  17. Chou P, Chien T, Yang T, Yeh Y, Chou W, Yeh C. Predicting Active NBA Players Most Likely to Be Inducted into the Basketball Hall of Famers Using Artificial Neural Networks in Microsoft Excel: Development and Usability Study. International Journal of Environmental Research and Public Health 2021;18(8):4256 View
  18. Manco L, Maffei N, Strolin S, Vichi S, Bottazzi L, Strigari L. Basic of machine learning and deep learning in imaging for medical physicists. Physica Medica 2021;83:194 View
  19. Sunarti S, Fadzlul Rahman F, Naufal M, Risky M, Febriyanto K, Masnina R. Artificial intelligence in healthcare: opportunities and risk for future. Gaceta Sanitaria 2021;35:S67 View
  20. Idowu O, Ilesanmi A, Li X, Samuel O, Fang P, Li G. An integrated deep learning model for motor intention recognition of multi-class EEG Signals in upper limb amputees. Computer Methods and Programs in Biomedicine 2021;206:106121 View
  21. Dagi T, Barker F, Glass J. Machine Learning and Artificial Intelligence in Neurosurgery: Status, Prospects, and Challenges. Neurosurgery 2021;89(2):133 View
  22. Huang Z. Challenges and issues in the development of the human healthcare system. Journal of Intelligent & Fuzzy Systems 2021:1 View
  23. Liu F, Chen C, Cheng C, Wu C, Hsu C, Fu C, Chen S, Liao C, Lee M. Automatic Hip Detection in Anteroposterior Pelvic Radiographs—A Labelless Practical Framework. Journal of Personalized Medicine 2021;11(6):522 View
  24. Aoyama Y, Maruko I, Kawano T, Yokoyama T, Ogawa Y, Maruko R, Iida T, Vavvas D. Diagnosis of central serous chorioretinopathy by deep learning analysis of en face images of choroidal vasculature: A pilot study. PLOS ONE 2021;16(6):e0244469 View
  25. Högqvist Tabor V, Högqvist Tabor M, Keestra S, Parrot J, Alvergne A. Improving the Quality of Life of Patients with an Underactive Thyroid Through mHealth: A Patient-Centered Approach. Women's Health Reports 2021;2(1):182 View
  26. Pereira T, Morgado J, Silva F, Pelter M, Dias V, Barros R, Freitas C, Negrão E, Flor de Lima B, Correia da Silva M, Madureira A, Ramos I, Hespanhol V, Costa J, Cunha A, Oliveira H. Sharing Biomedical Data: Strengthening AI Development in Healthcare. Healthcare 2021;9(7):827 View
  27. Mac T, Hung N. Automated pill quality inspection using deep learning. International Journal of Modern Physics B 2021;35(14n16):2140050 View

Books/Policy Documents

  1. Mathew P, Pillai A. Enabling AI Applications in Data Science. View
  2. . Cloud-Based M-Health Systems for Vein Image Enhancement and Feature Extraction. View
  3. Kose U, Deperlioglu O, Alzubi J, Patrut B. Deep Learning for Medical Decision Support Systems. View
  4. Mahapatra D, Ray R, Dash S. Technical Advancements of Machine Learning in Healthcare. View