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

Wearables and MHealth Viewpoints (62) Artificial Intelligence (665) Clinical Information and Decision Making (902) Tutorial (46) Big Data (169) Decision Support for Health Professionals (736) Machine Learning (1004)

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

Authors of this article:

Igbe Tobore 1, 2 Author Orcid Image ;   Jingzhen Li 1 Author Orcid Image ;   Liu Yuhang 1 Author Orcid Image ;   Yousef Al-Handarish 1 Author Orcid Image ;   Abhishek Kandwal 1 Author Orcid Image ;   Zedong Nie 1 Author Orcid Image ;   Lei Wang 1 Author Orcid Image
Article Authors Cited by (73) Tweetations (2) Metrics

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 2021;60(1):199 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 2022;15(3):1531 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. 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
  28. Hunter B, Reis S, Campbell D, Matharu S, Ratnakumar P, Mercuri L, Hindocha S, Kalsi H, Mayer E, Glampson B, Robinson E, Al-Lazikani B, Scerri L, Bloch S, Lee R. Development of a Structured Query Language and Natural Language Processing Algorithm to Identify Lung Nodules in a Cancer Centre. Frontiers in Medicine 2021;8 View
  29. Hassannataj Joloudari J, Mojrian S, Nodehi I, Mashmool A, Kiani Zadegan Z, Khanjani Shirkharkolaie S, Alizadehsani R, Tamadon T, Khosravi S, Akbari Kohnehshari M, Hassannatajjeloudari E, Sharifrazi D, Mosavi A, Loh H, Tan R, Acharya U. Application of artificial intelligence techniques for automated detection of myocardial infarction: a review. Physiological Measurement 2022;43(8):08TR01 View
  30. Das T, Gohain L, Kakoty N, Malarvili M, Widiyanti P, Kumar G. Hierarchical approach for fusion of electroencephalography and electromyography for predicting finger movements and kinematics using deep learning. Neurocomputing 2023;527:184 View
  31. Costa J, Silva-Correia J, Reis R, Oliveira J. Deep learning in bioengineering and biofabrication: a powerful technology boosting translation from research to clinics. Journal of 3D Printing in Medicine 2021;5(4):191 View
  32. Aledhari M, Razzak R, Qolomany B, Al-Fuqaha A, Saeed F. Biomedical IoT: Enabling Technologies, Architectural Elements, Challenges, and Future Directions. IEEE Access 2022;10:31306 View
  33. Hsu C, Chien T, Yan Y. An application for classifying perceptions on my health bank in Taiwan using convolutional neural networks and web-based computerized adaptive testing. Medicine 2021;100(52):e28457 View
  34. Adak A, Pradhan B, Shukla N, Alamri A. Unboxing Deep Learning Model of Food Delivery Service Reviews Using Explainable Artificial Intelligence (XAI) Technique. Foods 2022;11(14):2019 View
  35. Junaid S, Imam A, Abdulkarim M, Surakat Y, Balogun A, Kumar G, Shuaibu A, Garba A, Sahalu Y, Mohammed A, Mohammed T, Abdulkadir B, Abba A, Iliyasu Kakumi N, Hashim A. Recent Advances in Artificial Intelligence and Wearable Sensors in Healthcare Delivery. Applied Sciences 2022;12(20):10271 View
  36. Jameela T, Athotha K, Singh N, Gunjan V, Kahali S, Roy S. Deep Learning and Transfer Learning for Malaria Detection. Computational Intelligence and Neuroscience 2022;2022:1 View
  37. Maqsood S, Xu S, Tran S, Garg S, Springer M, Karunanithi M, Mohawesh R. A survey: From shallow to deep machine learning approaches for blood pressure estimation using biosensors. Expert Systems with Applications 2022;197:116788 View
  38. Akbarian S, Nelder M, Russell C, Cawston T, Moreno L, Patel S, Allen V, Dolatabadi E. A Computer Vision Approach to Identifying Ticks Related to Lyme Disease. IEEE Journal of Translational Engineering in Health and Medicine 2022;10:1 View
  39. Peyret R, alSaeed D, Khelifi F, Al-Ghreimil N, Al-Baity H, Bouridane A. Convolutional Neural Network–Based Automatic Classification of Colorectal and Prostate Tumor Biopsies Using Multispectral Imagery: System Development Study. JMIR Bioinformatics and Biotechnology 2022;3(1):e27394 View
  40. Berdutin V, Abaeva O, Romanova T, Romanov S. Achievements and prospects for the application of artificial intelligence technologies in medicine: an overview. Part 1. Sociology of Medicine 2023;21(1):83 View
  41. Görtz M, Byczkowski M, Rath M, Schütz V, Reimold P, Gasch C, Simpfendörfer T, März K, Seitel A, Nolden M, Ross T, Mindroc-Filimon D, Michael D, Metzger J, Onogur S, Speidel S, Mündermann L, Fallert J, Müller M, von Knebel Doeberitz M, Teber D, Seitz P, Maier-Hein L, Duensing S, Hohenfellner M. A Platform and Multisided Market for Translational, Software-Defined Medical Procedures in the Operating Room (OP 4.1): Proof-of-Concept Study. JMIR Medical Informatics 2022;10(1):e27743 View
  42. Taimoor N, Rehman S. Reliable and Resilient AI and IoT-Based Personalised Healthcare Services: A Survey. IEEE Access 2022;10:535 View
  43. Ali O, AlAhmad A, Kahtan H. A review of advanced technologies available to improve the healthcare performance during COVID-19 pandemic. Procedia Computer Science 2023;217:205 View
  44. Musa N, Gital A, Aljojo N, Chiroma H, Adewole K, Mojeed H, Faruk N, Abdulkarim A, Emmanuel I, Folawiyo Y, Ogunmodede J, Oloyede A, Olawoyin L, Sikiru I, Katb I. A systematic review and Meta-data analysis on the applications of Deep Learning in Electrocardiogram. Journal of Ambient Intelligence and Humanized Computing 2022 View
  45. Tobore I, Kandwal A, Li J, Yan Y, Omisore O, Enitan E, Sinan L, Yuhang L, Wang L, Nie Z. Towards adequate prediction of prediabetes using spatiotemporal ECG and EEG feature analysis and weight-based multi-model approach. Knowledge-Based Systems 2020;209:106464 View
  46. Lee J, Kim S, Kim K, Chai Y, Yu H, Kim S, Choi J, Chung Y, Lee K, Yi K. Assessment of Inter-Institutional Post-Operative Hypoparathyroidism Status Using a Common Data Model. Journal of Clinical Medicine 2021;10(19):4454 View
  47. Ojokoh B, Aribisala B, Sarumi O, Gabriel A, Omisore O, Taiwo A, Igbe T, Chukwuocha U, Yusuf T, Afolayan A, Babalola O, Adebayo T, Afolabi O. Contact Tracing Strategies for COVID-19 Prevention and Containment: A Scoping Review. Big Data and Cognitive Computing 2022;6(4):111 View
  48. Nigam R, Rao M, Rian Dias N, Hariharan A, Choraria A, Tendolkar A, Manohara Pai M. Grow-IoT (smart analytics app for comprehensive plant health analysis and remote farm monitoring using smart sensors). Journal of Physics: Conference Series 2022;2161(1):012059 View
  49. Mishra D, Kaur K, Gurnani B, Heda A, Dwivedi K. Clinical and diagnostic color-coding in ophthalmology - An indispensable educational tool for ophthalmologists. Indian Journal of Ophthalmology 2022;70(9):3191 View
  50. Keddy K, Saha S, Kariuki S, Kalule J, Qamar F, Haq Z, Okeke I. Using big data and mobile health to manage diarrhoeal disease in children in low-income and middle-income countries: societal barriers and ethical implications. The Lancet Infectious Diseases 2022;22(5):e130 View
  51. Zwick B, Bourantas G, Safdar S, Joldes G, Hyde D, Warfield S, Wittek A, Miller K. Patient-specific solution of the electrocorticography forward problem in deforming brain. NeuroImage 2022;263:119649 View
  52. Ali A, Khan Mashwani W, Naeem S, Irfan Uddin M, Kumam W, Kumam P, Alrabaiah H, Jamal F, Chesneau C. COVID-19 Infected Lung Computed Tomography Segmentation and Supervised Classification Approach. Computers, Materials & Continua 2021;68(1):391 View
  53. Ali O, Abdelbaki W, Shrestha A, Elbasi E, Alryalat M, Dwivedi Y. A systematic literature review of artificial intelligence in the healthcare sector: Benefits, challenges, methodologies, and functionalities. Journal of Innovation & Knowledge 2023;8(1):100333 View
  54. Parviz M, Brieghel C, Agius R, Niemann C. Prediction of clinical outcome in CLL based on recurrent gene mutations, CLL-IPI variables, and (para)clinical data. Blood Advances 2022;6(12):3716 View
  55. Kumar M, Nguyen T, Kaur J, Singh T, Soni D, Singh R, Kumar P. Opportunities and challenges in application of artificial intelligence in pharmacology. Pharmacological Reports 2023;75(1):3 View
  56. Selvakanmani S, B A, Devi G, Misra S, R J, Perli S. Deep learning approach to solve image retrieval issues associated with IOT sensors. Measurement: Sensors 2022;24:100458 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
  5. Gaur N, Dharwadkar R, Thomas J. Deep Learning for Targeted Treatments. View
  6. Dargad S, Thakkar P, Giri S. Computing Science, Communication and Security. View
  7. Mac T. Proceedings of 10th International Conference on Mechatronics and Control Engineering. View
  8. Kumar S, Pooja , Kumar S, Veer K. Machine Learning Algorithms for Signal and Image Processing. View
  9. Nova S, Rahman M, Hosen A. Rhythms in Healthcare. View
  10. Shastry K, Sanjay H, Lakshmi M, Preetham N. Bioinformatics and Medical Applications. View
  11. Aditya Shastry K, Sanjay H, Lakshmi M, Preetham N. Blockchain and Deep Learning. View
  12. Mishra A, Mohapatra S, Bisoy S. Augmented Intelligence in Healthcare: A Pragmatic and Integrated Analysis. View
  13. Olaniyan O, Adetunji C, Adeyomoye O, Dare A, Adeniyi M, Enoch A. Artificial Intelligence for Neurological Disorders. View