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Prediction Model of Deep Learning for Ambulance Transports in Kesennuma City by Meteorological Data

Authors Watanabe O, Narita N, Katsuki M, Ishida N, Cai S, Otomo H, Yokota K

Received 23 November 2020

Accepted for publication 14 January 2021

Published 28 January 2021 Volume 2021:13 Pages 23—32

DOI https://doi.org/10.2147/OAEM.S293551

Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 2

Editor who approved publication: Dr Hans-Christoph Pape


Ohmi Watanabe,1 Norio Narita,2 Masahito Katsuki,2 Naoya Ishida,1 Siqi Cai,1 Hiroshi Otomo,3 Kenichi Yokota3

1Kesennuma City Hospital, Kesennuma, Miyagi 988-0181, Japan; 2Department of Neurosurgery, Kesennuma City Hospital, Kesennuma, Miyagi 988-0181, Japan; 3Department of Surgery, Kesennuma City Hospital, Kesennuma, Miyagi 988-0181, Japan

Correspondence: Norio Narita
Department of Neurosurgery, Kesennuma City Hospital, 8-2, Akaiwa-Suginosawa, Kesennuma, Miyagi, Japan
, 988-0181 Tel/ Fax +81-226-22-7100
Email nnarita@mbr.nifty.com

Purpose: With the aging population in Japan, the prediction of ambulance transports is needed to save the limited medical resources. Some meteorological factors were risks of ambulance transports, but it is difficult to predict in a classically statistical way because Japan has 4 seasons. We tried to make prediction models for ambulance transports using the deep learning (DL) framework, Prediction One (Sony Network Communications Inc., Tokyo, Japan), with the meteorological and calendarial variables.
Materials and Methods: We retrospectively investigated the daily ambulance transports and meteorological data between 2017 and 2019. First, to confirm their association, we performed classically statistical analysis. Second, to test the DL framework’s utility for ambulance transports prediction, we made 3 prediction models for daily ambulance transports (total daily ambulance transports more than 5 or not, cardiopulmonary arrest (CPA), and trauma) using meteorological and calendarial factors and evaluated their accuracies by internal cross-validation.
Results: During the 1095 days of 3 years, the total ambulance transports were 5948, including 240 CPAs and 337 traumas. Cardiogenic CPA accounted for 72.3%, according to the Utstein classification. The relation between ambulance transports and meteorological parameters by polynomial curves were statistically obtained, but their r2s were small. On the other hand, all DL-based prediction models obtained satisfactory accuracies in the internal cross-validation. The areas under the curves obtained from each model were all over 0.947.
Conclusion: We could statistically make polynomial curves between the meteorological variables and the number of ambulance transport. We also preliminarily made DL-based prediction models. The DL-based prediction for daily ambulance transports would be used in the future, leading to solving the lack of medical resources in Japan.

Keywords: ambulance transport, cardiopulmonary arrest, deep learning, meteorological factors, trauma

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