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

Machine Learning (1738) Artificial Intelligence (1740) Public Health (224) Infectious Diseases (non-STD/STI) (1657)

Published on in Vol 9, No 1 (2021): January

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/24924, first published .
Machine Learning Prediction of Foodborne Disease Pathogens: Algorithm Development and Validation Study

Machine Learning Prediction of Foodborne Disease Pathogens: Algorithm Development and Validation Study

Machine Learning Prediction of Foodborne Disease Pathogens: Algorithm Development and Validation Study

Authors of this article:

Hanxue Wang1, 2 Author Orcid Image ;   Wenjuan Cui1 Author Orcid Image ;   Yunchang Guo3 Author Orcid Image ;   Yi Du1, 2 Author Orcid Image ;   Yuanchun Zhou1, 2 Author Orcid Image
Article Authors Cited by (25) Tweetations (1) Metrics

Journals

  1. Du Y, Guo Y. Machine learning techniques and research framework in foodborne disease surveillance system. Food Control 2022;131:108448 View
  2. Benefo E, Karanth S, Pradhan A. Applications of advanced data analytic techniques in food safety and risk assessment. Current Opinion in Food Science 2022;48:100937 View
  3. Bai X, Chen G, Wang Z, Xie G, Deng M, Xu H. Simultaneous detection of Bacillus cereus and Staphylococcus aureus by teicoplanin functionalized magnetic beads combined with triplex PCR. Food Control 2022;132:108531 View
  4. Qian C, Murphy S, Orsi R, Wiedmann M. How Can AI Help Improve Food Safety?. Annual Review of Food Science and Technology 2023;14(1):517 View
  5. Chen Y, Wu C, Zhang Q, Wu D. Review of visual analytics methods for food safety risks. npj Science of Food 2023;7(1) View
  6. Zheng Y, Gracia A, Hu L. Predicting Foodborne Disease Outbreaks with Food Safety Certifications: Econometric and Machine Learning Analyses. Journal of Food Protection 2023;86(9):100136 View
  7. Kumar Y, Kaur I, Mishra S. Foodborne Disease Symptoms, Diagnostics, and Predictions Using Artificial Intelligence-Based Learning Approaches: A Systematic Review. Archives of Computational Methods in Engineering 2024;31(2):553 View
  8. Talari G, Nag R, O'Brien J, McNamara C, Cummins E. A data-driven approach for prioritising microbial and chemical hazards associated with dairy products using open-source databases. Science of The Total Environment 2024;908:168456 View
  9. Zhang L, Chen Q, Xiong S, Zhu S, Tian J, Li J, Guo H. Mushroom poisoning outbreaks in Guizhou Province, China: a prediction study using SARIMA and Prophet models. Scientific Reports 2023;13(1) View
  10. Akinsulie O, Idris I, Aliyu V, Shahzad S, Banwo O, Ogunleye S, Olorunshola M, Okedoyin D, Ugwu C, Oladapo I, Gbadegoye J, Akande Q, Babawale P, Rostami S, Soetan K. The potential application of artificial intelligence in veterinary clinical practice and biomedical research. Frontiers in Veterinary Science 2024;11 View
  11. Gbashi S, Njobeh P. Enhancing Food Integrity through Artificial Intelligence and Machine Learning: A Comprehensive Review. Applied Sciences 2024;14(8):3421 View
  12. Panaligan D, Sy I, Sarza R. Harnessing artificial intelligence in microbial food safety: global progress and implications in the ASEAN region. International Journal of Food Science and Technology 2024;59(10):7754 View
  13. Onyeaka H, Akinsemolu A, Miri T, Nnaji N, Emeka C, Tamasiga P, Pang G, Al-sharify Z. Advancing food security: The role of machine learning in pathogen detection. Applied Food Research 2024;4(2):100532 View
  14. Xu D, Chan W, Haron H, Nies H, Moorthy K. From COVID-19 to monkeypox: a novel predictive model for emerging infectious diseases. BioData Mining 2024;17(1) View
  15. Radio S, Di Marsico M, Bersani C, Malinverni R, Casacuberta J, Corpetti C, Cigliano R, Sanseverino W. Development of a roadmap for action on the application of Omics and associated Bioinformatics Approaches in Risk Assessment. EFSA Supporting Publications 2024;21(10) View
  16. Rugji J, Erol Z, Taşçı F, Musa L, Hamadani A, Gündemir M, Karalliu E, Siddiqui S. Utilization of AI – reshaping the future of food safety, agriculture and food security – a critical review. Critical Reviews in Food Science and Nutrition 2024:1 View
  17. Dhal S, Kar D. Leveraging artificial intelligence and advanced food processing techniques for enhanced food safety, quality, and security: a comprehensive review. Discover Applied Sciences 2025;7(1) View
  18. Wang Z, Liang P, Zhai J, Wu B, Chen X, Ding F, Chen Q, Sun B. Efficient detection of foodborne pathogens via SERS and deep learning: An ADMIN-optimized NAS-Unet approach. Journal of Hazardous Materials 2025;489:137581 View
  19. Liu J, Hong W, Zhao J, Xue H, Soteyome T, Yuan L, Seneviratne G, Xu Z. Establishment and evaluation of a rapid detection method on viable cells of Salmonella enterica: A potential POCT applicable in various food systems. LWT 2025;223:117692 View
  20. Tang Y, Han Y, Zhou W. Towards accurate food safety risks prediction via context-enhanced heterogeneous GNN. Applied Food Research 2025;5(1):100924 View

Books/Policy Documents

  1. Chen X, Wang H. Data Science. View
  2. Liu J, Bensimon J, Lu X. Smart Food Safety. View
  3. Tsoumtsa Meda L, Lagarde J, Guillier L, Roussel S, Douarre P. Foodborne Bacterial Pathogens. View
  4. Stazi A. GMOs, Food Traceability and RegTech. View

Conference Proceedings

  1. Shi Y, Bai Y, Zhang T, Liu W. 2022 4th International Conference on Intelligent Information Processing (IIP). Research on multi-hop reasoning question and answer model for foodborne disease incidents View