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

Digital Health Reviews (1724) Machine Learning (2076) Clinical Informatics (1325) Electronic Health Records (1319) Artificial Intelligence (2129) Big Data (307)

Published on in Vol 26 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/48320, first published .
The Use of Deep Learning and Machine Learning on Longitudinal Electronic Health Records for the Early Detection and Prevention of Diseases: Scoping Review

The Use of Deep Learning and Machine Learning on Longitudinal Electronic Health Records for the Early Detection and Prevention of Diseases: Scoping Review

The Use of Deep Learning and Machine Learning on Longitudinal Electronic Health Records for the Early Detection and Prevention of Diseases: Scoping Review

Authors of this article:

Laura Swinckels1, 2, 3, 4 Author Orcid Image ;   Frank C Bennis5, 6, 7 Author Orcid Image ;   Kirsten A Ziesemer8 Author Orcid Image ;   Janneke F M Scheerman2, 3 Author Orcid Image ;   Harmen Bijwaard3 Author Orcid Image ;   Ander de Keijzer4, 9 Author Orcid Image ;   Josef Jan Bruers1, 10 Author Orcid Image
Article Authors Cited by (28) Tweetations (4) Metrics

Journals

  1. Pan L, Mu L, Lei H, Miao S, Hu X, Tang Z, Chen W, Wang X. Predicting survival benefits of immune checkpoint inhibitor therapy in lung cancer patients: a machine learning approach using real-world data. International Journal of Clinical Pharmacy 2025;47(4):981 View
  2. Swinckels L, de Keijzer A, Loos B, Applegate R, Kookal K, Kalenderian E, Bijwaard H, Bruers J. A personalized periodontitis risk based on nonimage electronic dental records by machine learning. Journal of Dentistry 2025;153:105469 View
  3. Ziegler J, Erpenbeck M, Fuchs T, Saibold A, Volkmer P, Schmidt G, Eicher J, Pallaoro P, De Souza Falguera R, Aubele F, Hagedorn M, Vansovich E, Raffler J, Ringshandl S, Kerscher A, Maurer J, Kühnel B, Schenkirsch G, Kampf M, Kapsner L, Ghanbarian H, Spengler H, Soto-Rey I, Albashiti F, Hellwig D, Ertl M, Fette G, Kraska D, Boeker M, Prokosch H, Gulden C. Bridging Data Silos in Oncology with Modular Software for Federated Analysis on Fast Healthcare Interoperability Resources: Multisite Implementation Study. Journal of Medical Internet Research 2025;27:e65681 View
  4. Ding H, Xia W, Zhou Y, Wei L, Feng Y, Wang Z, Song X, Li R, Mao Q, Chen B, Wang H, Huang X, Zhu B, Jiang D, Sun J, Dong G, Jiang F. Evaluation and practical application of prompt-driven ChatGPTs for EMR generation. npj Digital Medicine 2025;8(1) View
  5. Matsuzaka Y, Yashiro R. The Diagnostic Classification of the Pathological Image Using Computer Vision. Algorithms 2025;18(2):96 View
  6. Stasolla F, Curcio E, Passaro A, Di Gioia M, Zullo A, Martini E. Exploring the Combination of Serious Games, Social Interactions, and Virtual Reality in Adolescents with ASD: A Scoping Review. Technologies 2025;13(2):76 View
  7. Wen J, Wei W, Zhang L, Xu J, Wang X, Chen S, Li J, Du K, Chang Y. Intelligent characterization multi-components in Yangxinshi tablet by online comprehensive two-dimensional liquid chromatography-quadrupole time-of-flight mass spectrometry combined with deep learning-assisted mass defect filtering classification and multidimensional data annotation strategy. Talanta 2025;290:127821 View
  8. Fawaz P, El Sayegh P, Vande Vannet B. Artificial intelligence in revolutionizing orthodontic practice. World Journal of Methodology 2025;15(3) View
  9. Stasolla F, Passaro A, Curcio E, Di Gioia M, Zullo A, Dragone M, Martini E. Combined deep and reinforcement learning with gaming to promote healthcare in neurodevelopmental disorders: a new hypothesis. Frontiers in Human Neuroscience 2025;19 View
  10. Baizer L, Bures R, Nadkarni G, Reyes-Guzman C, Ladwa S, Cade B, Westover M, Durmer J, de Zambotti M, Desai M, Parekh A, Si B, Fernandez-Mendoza J, Minor K, Mazzotti D, Lee S, Katabi D, Kiss O, Spira A, Morris J, Seixas A, Kioumourtzoglou M, Bridges J, Brown M, Hale L, Purcell S. Big data approaches for novel mechanistic insights on sleep and circadian rhythms: a workshop summary. SLEEP 2025;48(6) View
  11. Pan Y, Wei M, Jin M, Liang Y, Yi T, Tu J, Wu S, Hu F, Liang C. An interpretable machine learning model based on optimal feature selection for identifying CT abnormalities in patients with mild traumatic brain injury. eClinicalMedicine 2025;82:103192 View
  12. Ou J, Zhang J, Alswadeh M, Zhu Z, Tang J, Sang H, Lu K. Advancing osteoarthritis research: the role of AI in clinical, imaging and omics fields. Bone Research 2025;13(1) View
  13. Feng S, Zhou M, Huang Z, Xiao X, Zhong B. A machine learning-based prediction model for colorectal liver metastasis. Clinical and Experimental Medicine 2025;25(1) View
  14. Wu W, Zhang Z, Wang S, Xin R, Yang D, Yao W, Hei Z, Chen C, Luo G. Novel machine learning models for the prediction of acute respiratory distress syndrome after liver transplantation. Frontiers in Artificial Intelligence 2025;8 View
  15. Tang J, Li Y, Luo K, Lai J, Yin X, Wu D. Application of the Bidirectional Encoder Representations from Transformers Model for Predicting the Abbreviated Injury Scale in Patients with Trauma: Algorithm Development and Validation Study. JMIR Formative Research 2025;9:e67311 View
  16. Jatobá A, de Castro-Nunes P, Palmieri P, Machado Araujo de Oliveira O, Passos Simões P, da Silva Fonseca V, de Carvalho P. Predictive estimations of health systems resilience using machine learning. BMC Medical Informatics and Decision Making 2025;25(1) View
  17. Zeng L, Liu R, Xiong L, Ho J. TransFed: cross-domain feature alignment for semi-supervised federated transfer learning. Machine Learning 2025;114(8) View
  18. Väyrynen E, Tirkkonen O, Tiensuu H, Suutala J, Anttonen V, Laitala M, Kukkola K, Karki S. A Machine Learning Algorithm With an Oversampling Technique in Limited Data Scenarios for the Prediction of Present and Future Restorative Treatment Need: Development and Validation Study. JMIR Medical Informatics 2025;13:e75117 View
  19. Fahmy A. Exploring the Role of AI in Predicting Chronic Disease Progression: Diabetes and Cardiovascular Diseases. Premier Journal of Public Health 2025 View
  20. Miller H, Goodin D, Frieboes H. An automated software methodology for biomedical statistics, data pre-processing, and machine learning. Computer Methods and Programs in Biomedicine 2026;273:109096 View
  21. Araujo C, Delpino F, Figueiredo L, Chiavegatto Filho A, Nunes B, Schuch H, Demarco F. Predicting negative self-rated oral health in adults using machine learning: A longitudinal study in Southern Brazil. Journal of Dentistry 2025;163:106164 View
  22. Dou Y, Liu Y, Zou H, Zeng W, Xu K, Peng S. A survey on electronic health record driven multimodal representation learning. Information Fusion 2026;127:103810 View
  23. Zhang Y, Han J, Chen H, Hu F, Huang Y, Tian G, Zhong D, Yang J. Deep learning-based fusion of nuclear segmentation features for microsatellite instability and tumor mutational burden prediction in digestive tract cancers: a multicenter validation study. Briefings in Bioinformatics 2025;26(6) View
  24. Kondrashov A, Loskutova E, Kurashov M. The digital profile of a physician as a new approach to improving recommendation systems for prescribing drugs. International Journal of Risk & Safety in Medicine 2025 View
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Books/Policy Documents

  1. . Creating Responsible Inquiring Digital Health Organizations. View