Abstract
Multridrug-resistant (MDR) bacteria are currently a serious threat to public health. They are primarily associated with hospital-acquired infections and their spread is related to an increase in the morbidity, mortality and healthcare cost. Therefore, their control and prevention is a priority problem today. In this control, the need of detecting MDR-bacteria outbreaks inside a hospital stands out. This complex process requires interweaving reports with temporal and spatial information. In this matter, computer-aided visualization techniques might play an important role, by helping explain and understand data-driven decision making. Thus, the hypothesis of this PhD thesis is that spatial-temporal modeling and visualization techniques allow clinicians to increase their confidence and comprehension of AI-based epidemiological analysis and prediction models. During the first phase of this PhD project, we have carried out a detailed analysis of what has been done on the application of spatial-temporal visualization techniques on epidemiological data. The results of this investigation have helped us identify the current trends and gaps in this field. Following this, we have implemented a simulation model and its simulator, with the objective of generating clinically realistic spatial-temporal data of infection outbreaks within hospitals.
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Acknowledgements
This work was partially funded by the SITSUS project (Ref: RTI2018-094832-B-I00), the CONFAINCE project (Ref: PID2021-122194OB-I00), supported by the Spanish Ministry of Science and Innovation, the Spanish Agency for Research (MCIN/AEI/10.13039/501100011033) and, as appropriate, by ERDF A way of making Europe. This research is also partially funded by the FPI program grant (Ref:PRE2019-089806).
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Kim, D., Juarez, J.M., Campos, M., Canovas-Segura, B. (2023). Visualization for Infection Analysis and Decision Support in Hospitals. In: Machado, J.M., et al. Distributed Computing and Artificial Intelligence, Special Sessions, 19th International Conference. DCAI 2022. Lecture Notes in Networks and Systems, vol 585. Springer, Cham. https://doi.org/10.1007/978-3-031-23210-7_15
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