Consolidation of massive medical emergency events with heterogeneous situational context data sources
The prevalence, spatiotemporal distribution, and category incidence of medical emergencies are rapidly changing worldwide. The current pandemic context and emerging trends in public health create the need for self-adapting Emergency Medical Services (EMS). Emergency occurrences and responses are int...
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| Main Authors: | , , , , |
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| Format: | text |
| Published: |
Animo Repository
2022
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| Subjects: | |
| Online Access: | https://animorepository.dlsu.edu.ph/faculty_research/13070 |
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| Institution: | De La Salle University |
| Summary: | The prevalence, spatiotemporal distribution, and category incidence of medical emergencies are rapidly changing worldwide. The current pandemic context and emerging trends in public health create the need for self-adapting Emergency Medical Services (EMS). Emergency occurrences and responses are intricately dependent on contextual factors, including weather, epidemic context, urban traffic, large-scale events, and demographics. In this context, monitoring emergency occurrences, medical responses, and their situational context is essential to optimize EMS efficiency and efficacy. In this work, we implement best practices in multidimensional database modelling to consolidate emergency event data with public sources of situational context for context-aware data analysis. The resulting design is able to address challenges pertaining to the massive, incomplete, and spatiotemporal nature of emergency event data and the heterogeneity of context sources and their varying spatiotemporal footprints. We present a study case on real-world medical emergency data from Portugal. The results show the efficient retrieval of data structures conducive to spatiotemporal data mining tasks. |
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