Abstract
The importance of the infusion drug optimization in patients undergoing general anesthesia has led to the implementation of automatic control loops and models to predict the state of the patient. The appearance of any anomaly during the anesthetic process may lead, for instance, to incorrect drug administration. This could produce undesirable side effects that can affect the patient postoperative and also reduce the safety of the patient in the operating room. This study evaluates different one-class intelligent techniques to detect anomalies in patients undergoing general anesthesia. Due to the difficulty of obtaining data from anomaly situations, artificial outliers are generated to check the performance of each classifier. The final results give good performance in general terms.
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Acknowledgments
This research is partially supported through the “Fundación Canaria de Investigación Sanitaria” (FUNCANIS) [ref: PIFUN23/18].
Jose M. Gonzalez-Cava’s research was supported by the Spanish Ministry of Education, Culture and Sport (www.mecd.gob.es), under the “Formación de Profesorado” grant FPU15/03347.
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Jove, E., Gonzalez-Cava, J.M., Casteleiro-Roca, JL., Quintián, H., Méndez-Pérez, J.A., Calvo-Rolle, J.L. (2020). Anomaly Detection on Patients Undergoing General Anesthesia. In: Martínez Álvarez, F., Troncoso Lora, A., Sáez Muñoz, J., Quintián, H., Corchado, E. (eds) International Joint Conference: 12th International Conference on Computational Intelligence in Security for Information Systems (CISIS 2019) and 10th International Conference on EUropean Transnational Education (ICEUTE 2019). CISIS ICEUTE 2019 2019. Advances in Intelligent Systems and Computing, vol 951. Springer, Cham. https://doi.org/10.1007/978-3-030-20005-3_15
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