Research Publications for Michael J Mayo

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Author's Publications

Publications ByMAYO, Michael J

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  • Wang, H., Chepulis, L., Paul, R. G., & Mayo, M. (2021). Metaheuristic optimization of insulin infusion protocols using historical data with validation using a patient simulator. Vietnam Journal of Computer Science, 8(2), 263-290. doi:10.1142/s2196888821500111

  • Wakes, S. J., Bauer, B. O., & Mayo, M. (2021). A preliminary assessment of machine learning algorithms for predicting CFD-simulated wind flow patterns over idealised foredunes. Journal of the Royal Society of New Zealand. doi:10.1080/03036758.2020.1868541

  • Wang, H., Chepulis, L., Paul, R. G., & Mayo, M. (2020). Metaheuristics for discovering favourable continuous intravenous insulin rate protocols from historical patient data. In P. Sitek, M. Pietranik, M. Krótkiewicz, & C. Srinilta (Eds.), Proc 12th Asian Conference on Intelligent Information and Database Systems (ACIIDS 2020) LNCS 12033 (pp. 157-169). Phuket, Thailand: Springer. doi:10.1007/978-3-030-41964-6_14

  • Yogarajan, V., Pfahringer, B., & Mayo, M. (2020). A review of automatic end-to-end de-Identification: Is high accuracy the only metric?. Applied Artificial Intelligence, 34(3), 251-269. doi:10.1080/08839514.2020.1718343

  • 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

  • Yogarajan, V., Gouk, H., Smith, T., Mayo, M., & Pfahringer, B. (2020). Comparing high dimensional word embeddings trained on medical text to bag-of-words for predicting medical codes. In P. Sitek, M. Petranik, M. Krótkiewicz, & C. Srinilta (Eds.), Proc 12th Asian Conference on Intelligent Information and Database Systems (ACIIDS 2020) LNCS 12033 (pp. 97-108). Phuket, Thailand: Springer. doi:10.1007/978-3-030-41964-6_9

  • Cherrier, N., Mayo, M., Poli, J. -P., Defurne, M., & Sabatié, F. (2020). Interpretable machine learning with bitonic generalized additive models and automatic feature construction. In A. Appice, G. Tsoumakas, Y. Manolopoulos, & S. Matwin (Eds.), Proc 23rd International Conference on Discovery Science (DS 2020), LNAI 12323 (pp. 386-402). Thessaloniki, Greece: Springer. doi:10.1007/978-3-030-61527-7_26

  • 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., & Frank, E. (2020). Improving naive Bayes for regression with optimised artificial surrogate data. Applied Artificial Intelligence, 34(6), 484--514. doi:10.1080/08839514.2020.1726615

  • Hébert-Losier, K., Hanzlíková, I., Zheng, C., Streeter, L., & Mayo, M. (2020). The 'DEEP' landing error scoring system. Applied Sciences (Switzerland), 10(3). doi:10.3390/app10030892

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