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Predicting Medical Resources Required to be Dispatched After Earthquake and Flood, Using Historical Data and Machine Learning Techniques: The COncORDE Emergency Medical Service Use Case

Predicting Medical Resources Required to be Dispatched After Earthquake and Flood, Using Historical Data and Machine Learning Techniques: The COncORDE Emergency Medical Service Use Case

Homer Papadopoulos, Antonis Korakis
Copyright: © 2020 |Pages: 29
ISBN13: 9781799825357|ISBN10: 1799825353|ISBN13 Softcover: 9781799825364|EISBN13: 9781799825371
DOI: 10.4018/978-1-7998-2535-7.ch003
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MLA

Papadopoulos, Homer, and Antonis Korakis. "Predicting Medical Resources Required to be Dispatched After Earthquake and Flood, Using Historical Data and Machine Learning Techniques: The COncORDE Emergency Medical Service Use Case." Improving the Safety and Efficiency of Emergency Services: Emerging Tools and Technologies for First Responders, edited by Information Resources Management Association, IGI Global, 2020, pp. 38-66. https://doi.org/10.4018/978-1-7998-2535-7.ch003

APA

Papadopoulos, H. & Korakis, A. (2020). Predicting Medical Resources Required to be Dispatched After Earthquake and Flood, Using Historical Data and Machine Learning Techniques: The COncORDE Emergency Medical Service Use Case. In I. Management Association (Ed.), Improving the Safety and Efficiency of Emergency Services: Emerging Tools and Technologies for First Responders (pp. 38-66). IGI Global. https://doi.org/10.4018/978-1-7998-2535-7.ch003

Chicago

Papadopoulos, Homer, and Antonis Korakis. "Predicting Medical Resources Required to be Dispatched After Earthquake and Flood, Using Historical Data and Machine Learning Techniques: The COncORDE Emergency Medical Service Use Case." In Improving the Safety and Efficiency of Emergency Services: Emerging Tools and Technologies for First Responders, edited by Information Resources Management Association, 38-66. Hershey, PA: IGI Global, 2020. https://doi.org/10.4018/978-1-7998-2535-7.ch003

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Abstract

This article presents a method to predict the medical resources required to be dispatched after large-scale disasters to satisfy the demand. The historical data of past incidents (earthquakes, floods) regarding the number of victims requested emergency medical services and hospitalisation, simulation tools, web services and machine learning techniques have been combined. The authors adopted a twofold approach: a) use of web services and simulation tools to predict the potential number of victims and b) use of historical data and self-trained algorithms to “learn” from these data and provide relative predictions. Comparing actual and predicted victims needed hospitalisation showed that the proposed models can predict the medical resources required to be dispatched with acceptable errors. The results are promoting the use of electronic platforms able to coordinate an emergency medical response since these platforms can collect big heterogeneous datasets necessary to optimise the performance of the suggested algorithms.

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