A bayesian approach for the continuous monitoring of the prediction of the physiological evolution of a crisis victim: A decision support system

IF 4.2 1区地球科学 Q1 GEOSCIENCES, MULTIDISCIPLINARY

International journal of disaster risk reduction Pub Date : 2024-10-09 DOI:10.1016/j.ijdrr.2024.104890

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引用次数: 0

Abstract

Catastrophic events like earthquakes demand innovative tools for crisis management. Mathematical modeling and decision support systems (DSSs) have proved crucial for understanding, predicting and mitigating disaster impact. The quantification of complex phenomena through probabilistic models, to estimate the likelihood of events, provides actionable insights that are essential for disaster risk reduction (DRR).

The present work stems from research conducted within the framework of the Search & Rescue (S&R) project (H2020-SU-SEC-2019), in particular from the development of the PHYSIO DSS module, the medical component of the S&R Decision Support System (DSS). The PHYSIO DSS focuses on predicting the physiological evolution of crisis victims: using a Bayesian approach, it incorporates real-time field observations to forecast patient conditions. This enables the prediction of the evolution of physiological compensation, allowing efficient resource allocation and timely interventions. By providing real-time insights into victim severity, PHYSIO DSS empowers medical personnel to prioritize treatment, potentially saving lives. Its adaptability allows integration into different platforms, from crisis management systems to apps to personal health devices.

This tool has the potential to substantially enhance emergency response capability and overall disaster resilience by offering real-time, data-driven decision support.

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预测危机受害者生理演变的连续监测贝叶斯方法：决策支持系统

地震等灾难性事件需要创新的危机管理工具。事实证明，数学建模和决策支持系统（DSS）对于理解、预测和减轻灾害影响至关重要。通过概率模型对复杂现象进行量化，以估计事件发生的可能性，为降低灾害风险（DRR）提供了可操作的见解。本研究工作源于在搜索与救援（S&R）项目（H2020-SU-SEC-2019）框架内开展的研究，特别是S&R决策支持系统（DSS）的医疗部分--PHYSIO DSS模块的开发。PHYSIO DSS 的重点是预测危机受害者的生理变化：使用贝叶斯方法，结合实时现场观察来预测病人的状况。这样就能预测生理补偿的演变，从而实现有效的资源分配和及时干预。PHYSIO DSS 可实时了解受害者的严重程度，从而帮助医务人员确定治疗的优先次序，从而挽救生命。它的适应性允许集成到不同的平台，从危机管理系统到应用程序，再到个人健康设备。通过提供实时、数据驱动的决策支持，该工具有望大幅提高应急响应能力和整体抗灾能力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International journal of disaster risk reduction GEOSCIENCES, MULTIDISCIPLINARYMETEOROLOGY-METEOROLOGY & ATMOSPHERIC SCIENCES

CiteScore

8.70

自引率

18.00%

发文量

688

审稿时长

79 days

期刊介绍： The International Journal of Disaster Risk Reduction (IJDRR) is the journal for researchers, policymakers and practitioners across diverse disciplines: earth sciences and their implications; environmental sciences; engineering; urban studies; geography; and the social sciences. IJDRR publishes fundamental and applied research, critical reviews, policy papers and case studies with a particular focus on multi-disciplinary research that aims to reduce the impact of natural, technological, social and intentional disasters. IJDRR stimulates exchange of ideas and knowledge transfer on disaster research, mitigation, adaptation, prevention and risk reduction at all geographical scales: local, national and international. Key topics:- -multifaceted disaster and cascading disasters -the development of disaster risk reduction strategies and techniques -discussion and development of effective warning and educational systems for risk management at all levels -disasters associated with climate change -vulnerability analysis and vulnerability trends -emerging risks -resilience against disasters. The journal particularly encourages papers that approach risk from a multi-disciplinary perspective.