Research Publications for Michael J Mayo

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Author's Publications

Publications ByMAYO, Michael J

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  • Erandathi, M. A., Wang, W. Y. C., & Mayo, M. (2020). Predicting diabetes mellitus and its complications through a graph-based risk scoring system. In Proc 4th International Conference on Medical and Health Informatics (ICMHI 2020) (pp. 1-7). Kamakura City, Japan: ACM. doi:10.1145/3418094.3418115

  • Lu, Y., Koay, A., & Mayo, M. (2020). In silico comparison of continuous glucose monitor failure mode strategies for an artificial pancreas. In K. Bach, R. Bunescu, C. Marling, & N. Wiratunga (Eds.), Proc 5th International Workshop on Knowledge Discovery in Healthcare Data (KDH 2020) Vol. 2675 (pp. 53-57). Santiago de Compostela, Spain & Virtually: CEUR Workshop Proceedings. Retrieved from http://ceur-ws.org/Vol-2675/paper8.pdf

  • Mayo, M., & Koutny, T. (2020). Neural multi-class classification approach to blood glucose level forecasting with prediction uncertainty visualisation. In K. Bach, R. Bunescu, C. Marling, & N. Wiratunga (Eds.), Proc 5th International Workshop on Knowledge Discovery in Healthcare Data (KDH 2020) Vol. 2675 (pp. 80-84). Santiago de Compostela, Spain & Virtually: CEUR Workshop Proceedings. Retrieved from http://ceur-ws.org/Vol-2675/paper13.pdf

  • Mayo, M., Chepulis, L., & Paul, R. G. (2019). Glycemic-aware metrics and oversampling techniques for predicting blood glucose levels using machine learning. PLOS ONE, 14(12), e0225613. doi:10.1371/journal.pone.0225613

  • Burnside, M., Crocket, H., Mayo, M., Pickering, J., Tappe, A., & de Bock, M. (2019). Do it yourself automated insulin delivery: A leading example of the democratization of medicine. Journal of Diabetes Science and Technology. doi:10.1177/1932296819890623

  • Mayo, M., & Yogarajan, V. (2019). A nearest neighbour-based analysis to identify patients from continuous glucose monitor data. In N. T. Nguyen, F. L. Gaol, T. P. Hong, & B. Trawinski (Eds.), Intelligent Information and Database Systems. ACIIDS 2019. Lecture Notes in Computer Science Vol. 11432 (pp. 349-360). Cham: Springer. doi:10.1007/978-3-030-14802-7_30

  • Daoud, M., Mayo, M., & Cunningham, S. J. (2019). RBFA: Radial Basis Function Autoencoders. In 2019 IEEE Congress on Evolutionary Computation (IEEE CEC 2019) (pp. 2966-2973). Wellington, NZ. doi:10.1109/CEC.2019.8790041

  • Mayo, M. (2019). Improving the robustness of the glycemic variability percentage metric to sensor dropouts in continuous glucose monitor data. In N. T. Nguyen, F. L. Gaol, T. P. Hong, & B. Trawinski (Eds.), Intelligent Information and Database Systems. ACIIDS 2019 LNCS 11432 (pp. 373-384). Cham: Springer. doi:10.1007/978-3-030-14802-7_32

  • Daoud, M., & Mayo, M. (2019). A survey of neural network-based cancer prediction models from microarray data. Artificial Intelligence in Medicine, 97, 204-214. doi:10.1016/j.artmed.2019.01.006

  • Hebert-Losier, K., Hanzlíková, I., Zheng, C., Streeter, L., & Mayo, M. (2019). The Deep Landing Error Scoring System calculation method can make an important difference!. In XXVII Congress of the International Society of Biomechanics. Calgary, Canada.

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