Design and implementation of a smart Internet of Things chest pain center based on deep learning
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. T...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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AIMS Press
2023-10-01
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| Series: | Mathematical Biosciences and Engineering |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2023840?viewType=HTML |
| _version_ | 1797634024624095232 |
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| author | Feng Li Zhongao Bi Hongzeng Xu Yunqi Shi Na Duan Zhaoyu Li |
| author_facet | Feng Li Zhongao Bi Hongzeng Xu Yunqi Shi Na Duan Zhaoyu Li |
| author_sort | Feng Li |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T12:01:59Z |
| format | Article |
| id | doaj.art-bb358898a66d485b9678d8b190eb5f24 |
| institution | Directory Open Access Journal |
| issn | 1551-0018 |
| language | English |
| last_indexed | 2024-03-11T12:01:59Z |
| publishDate | 2023-10-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj.art-bb358898a66d485b9678d8b190eb5f242023-11-08T01:25:34ZengAIMS PressMathematical Biosciences and Engineering1551-00182023-10-012010189871901110.3934/mbe.2023840Design and implementation of a smart Internet of Things chest pain center based on deep learningFeng Li0Zhongao Bi 1Hongzeng Xu2Yunqi Shi 3 Na Duan 4Zhaoyu Li51. School of Information and Electronic Engineering, Zhejiang Gongshang University, Hangzhou 310018, China 2. School of Computer Science and Engineering, Nanyang Technological University, 639798, Singapore1. School of Information and Electronic Engineering, Zhejiang Gongshang University, Hangzhou 310018, China3. Department of Cardiology, The People's Hospital of Liaoning Province, Liaoning, Shenyang 110011, China3. Department of Cardiology, The People's Hospital of Liaoning Province, Liaoning, Shenyang 110011, China3. Department of Cardiology, The People's Hospital of Liaoning Province, Liaoning, Shenyang 110011, China4. Department of Cardiology, The Second Affiliated Hospital Zhejiang University School of Medicine, Hangzhou 310000, ChinaThe 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.https://www.aimspress.com/article/doi/10.3934/mbe.2023840?viewType=HTMLchest pain centerinternet of things (iot)deep learning |
| spellingShingle | 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 Mathematical Biosciences and Engineering chest pain center internet of things (iot) deep learning |
| title | Design and implementation of a smart Internet of Things chest pain center based on deep learning |
| title_full | Design and implementation of a smart Internet of Things chest pain center based on deep learning |
| title_fullStr | Design and implementation of a smart Internet of Things chest pain center based on deep learning |
| title_full_unstemmed | Design and implementation of a smart Internet of Things chest pain center based on deep learning |
| title_short | Design and implementation of a smart Internet of Things chest pain center based on deep learning |
| title_sort | design and implementation of a smart internet of things chest pain center based on deep learning |
| topic | chest pain center internet of things (iot) deep learning |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2023840?viewType=HTML |
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