Design and implementation of a smart Internet of Things chest pain center based on deep learning

Feng Li; Zhongao Bi; Hongzeng Xu; Yunqi Shi; Na Duan; Zhaoyu Li; Feng Li; Zhongao Bi; Hongzeng Xu; Yunqi Shi; Na Duan; Zhaoyu Li

doi:10.3934/mbe.2023840

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 10: 18987-19011. doi: 10.3934/mbe.2023840

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Research article Special Issues

Design and implementation of a smart Internet of Things chest pain center based on deep learning

1.
School of Information and Electronic Engineering, Zhejiang Gongshang University, Hangzhou 310018, China
2.
School of Computer Science and Engineering, Nanyang Technological University, 639798, Singapore
3.
Department of Cardiology, The People's Hospital of Liaoning Province, Liaoning, Shenyang 110011, China
4.
Department of Cardiology, The Second Affiliated Hospital Zhejiang University School of Medicine, Hangzhou 310000, China

Academic Editor: Junxin Chen

Received: 05 July 2023 Revised: 13 August 2023 Accepted: 27 August 2023 Published: 10 October 2023

The data input process for most chest pain centers is not intelligent, requiring a lot of staff to manually input patient information. This leads to problems such as long processing times, high potential for errors, an inability to access patient data in a timely manner and an increasing workload. To address the challenge, an Internet of Things (IoT)-driven chest pain center is designed, which crosses the sensing layer, network layer and application layer. The system enables the construction of intelligent chest pain management through a pre-hospital app, Ultra-Wideband (UWB) positioning, and in-hospital treatment. The pre-hospital app is provided to emergency medical services (EMS) centers, which allows them to record patient information in advance and keep it synchronized with the hospital's database, reducing the time needed for treatment. UWB positioning obtains the patient's hospital information through the zero-dimensional base station and the corresponding calculation engine, and in-hospital treatment involves automatic acquisition of patient information through web and mobile applications. The system also introduces the Bidirectional Long Short-Term Memory (BiLSTM)-Conditional Random Field (CRF)-based algorithm to train electronic medical record information for chest pain patients, extracting the patient's chest pain clinical symptoms. The resulting data are saved in the chest pain patient database and uploaded to the national chest pain center. The system has been used in Liaoning Provincial People's Hospital, and its subsequent assistance to doctors and nurses in collaborative treatment, data feedback and analysis is of great significance.
- chest pain center,
- Internet of Things (IoT),
- deep learning
Citation: Feng Li, Zhongao Bi, Hongzeng Xu, Yunqi Shi, Na Duan, Zhaoyu Li. Design and implementation of a smart Internet of Things chest pain center based on deep learning[J]. Mathematical Biosciences and Engineering, 2023, 20(10): 18987-19011. doi: 10.3934/mbe.2023840

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Abstract

The data input process for most chest pain centers is not intelligent, requiring a lot of staff to manually input patient information. This leads to problems such as long processing times, high potential for errors, an inability to access patient data in a timely manner and an increasing workload. To address the challenge, an Internet of Things (IoT)-driven chest pain center is designed, which crosses the sensing layer, network layer and application layer. The system enables the construction of intelligent chest pain management through a pre-hospital app, Ultra-Wideband (UWB) positioning, and in-hospital treatment. The pre-hospital app is provided to emergency medical services (EMS) centers, which allows them to record patient information in advance and keep it synchronized with the hospital's database, reducing the time needed for treatment. UWB positioning obtains the patient's hospital information through the zero-dimensional base station and the corresponding calculation engine, and in-hospital treatment involves automatic acquisition of patient information through web and mobile applications. The system also introduces the Bidirectional Long Short-Term Memory (BiLSTM)-Conditional Random Field (CRF)-based algorithm to train electronic medical record information for chest pain patients, extracting the patient's chest pain clinical symptoms. The resulting data are saved in the chest pain patient database and uploaded to the national chest pain center. The system has been used in Liaoning Provincial People's Hospital, and its subsequent assistance to doctors and nurses in collaborative treatment, data feedback and analysis is of great significance.

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