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

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 & 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

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