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An Explainable Multimodal Fusion Approach for Mass Casualty Incidents

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Database and Expert Systems Applications - DEXA 2022 Workshops (DEXA 2022)

Abstract

During a Mass Casualty Incident, it is essential to make effective decisions to save lives and nursing the injured. This paper presents a work in progress on the design and development of an explainable decision support system, intended for the medical personnel and care givers, that capitalises on multiple modalities to achieve situational awareness and pre-hospital life support. Our novelty is two-fold: first, we use state-of-the-art techniques for combining static and time-series data in deep recurrent neural networks, and second we increase the trustworthiness of the system by enriching it with neurosymbolic explainable capabilities.

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Notes

  1. 1.

    http://bioportal.bioontology.org/ontologies/SNOMEDCT.

  2. 2.

    https://bioportal.bioontology.org/ontologies/ICD10.

  3. 3.

    https://www.nightingale-triage.eu/.

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Acknowledgements

This work has received funding from the European Union’s H2020 RIA projects NIGHTINGALE (101021957) and INGENIOUS (833435). Content reflects only the authors’ view and the Research Executive Agency (REA) and the European Commission are not responsible for any use that may be made of the information it contains.

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Correspondence to Zoe Vasileiou .

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Vasileiou, Z., Meditskos, G., Vrochidis, S., Bassiliades, N. (2022). An Explainable Multimodal Fusion Approach for Mass Casualty Incidents. In: Kotsis, G., et al. Database and Expert Systems Applications - DEXA 2022 Workshops. DEXA 2022. Communications in Computer and Information Science, vol 1633. Springer, Cham. https://doi.org/10.1007/978-3-031-14343-4_35

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  • DOI: https://doi.org/10.1007/978-3-031-14343-4_35

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-14342-7

  • Online ISBN: 978-3-031-14343-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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