Data governance and Gensini score automatic calculation for coronary angiography with deep-learning-based natural language extraction
With the widespread adoption of electronic health records, the amount of stored medical data has been increasing. Clinical data, often in the form of semi-structured or unstructured electronic medical records (EMRs), contains rich patient information. However, due to the use of natural language by p...
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AIMS Press
2024-02-01
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| Series: | Mathematical Biosciences and Engineering |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2024180?viewType=HTML |
| _version_ | 1797263736340217856 |
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| author | Feng Li Mingfeng Jiang Hongzeng Xu Yi Chen Feng Chen Wei Nie Li Wang |
| author_facet | Feng Li Mingfeng Jiang Hongzeng Xu Yi Chen Feng Chen Wei Nie Li Wang |
| author_sort | Feng Li |
| collection | DOAJ |
| description | With the widespread adoption of electronic health records, the amount of stored medical data has been increasing. Clinical data, often in the form of semi-structured or unstructured electronic medical records (EMRs), contains rich patient information. However, due to the use of natural language by physicians when composing these records, the effectiveness of traditional methods such as dictionaries, rule matching, and machine learning in the extraction of information from these unstructured texts falls short of clinical standards. In this paper, a novel deep-learning-based natural language extraction method is proposed to overcome current shortcomings in data governance and Gensini score automatic calculation in coronary angiography. A pre-trained model called bidirectional encoder representation from transformers (BERT) with strong text feature representation capabilities is employed as the feature representation layer. It is combined with bidirectional long short-term memory (BiLSTM) and conditional random field (CRF) models to extract both global and local features from the text. The study included an evaluation of the model on a dataset from a hospital in China and it was compared with another model to validate its practical advantages. Hence, the BiLSTM-CRF model was employed to automatically extract relevant coronary angiogram information from EMR texts. The achieved F1 score was 91.19, which is approximately 0.87 higher than the BERT-BiLSTM-CRF model. |
| first_indexed | 2024-04-25T00:17:45Z |
| format | Article |
| id | doaj.art-6953ab67b0d44c679080de22d42cab3e |
| institution | Directory Open Access Journal |
| issn | 1551-0018 |
| language | English |
| last_indexed | 2024-04-25T00:17:45Z |
| publishDate | 2024-02-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj.art-6953ab67b0d44c679080de22d42cab3e2024-03-13T01:23:23ZengAIMS PressMathematical Biosciences and Engineering1551-00182024-02-012134085410310.3934/mbe.2024180Data governance and Gensini score automatic calculation for coronary angiography with deep-learning-based natural language extractionFeng Li0Mingfeng Jiang1Hongzeng Xu2Yi Chen 3Feng Chen4Wei Nie5Li Wang61. School of Information and Electronic Engineering, Zhejiang Gongshang University, Hangzhou 310018, China1. School of Information and Electronic Engineering, Zhejiang Gongshang University, Hangzhou 310018, China2. Department of Cardiology, The People's Hospital of Liaoning Province, Liaoning, Shenyang 110011, China1. School of Information and Electronic Engineering, Zhejiang Gongshang University, Hangzhou 310018, China1. School of Information and Electronic Engineering, Zhejiang Gongshang University, Hangzhou 310018, China1. School of Information and Electronic Engineering, Zhejiang Gongshang University, Hangzhou 310018, China3. College of Marine Electrical Engineering, Dalian Maritime University, Dalian 116026, ChinaWith the widespread adoption of electronic health records, the amount of stored medical data has been increasing. Clinical data, often in the form of semi-structured or unstructured electronic medical records (EMRs), contains rich patient information. However, due to the use of natural language by physicians when composing these records, the effectiveness of traditional methods such as dictionaries, rule matching, and machine learning in the extraction of information from these unstructured texts falls short of clinical standards. In this paper, a novel deep-learning-based natural language extraction method is proposed to overcome current shortcomings in data governance and Gensini score automatic calculation in coronary angiography. A pre-trained model called bidirectional encoder representation from transformers (BERT) with strong text feature representation capabilities is employed as the feature representation layer. It is combined with bidirectional long short-term memory (BiLSTM) and conditional random field (CRF) models to extract both global and local features from the text. The study included an evaluation of the model on a dataset from a hospital in China and it was compared with another model to validate its practical advantages. Hence, the BiLSTM-CRF model was employed to automatically extract relevant coronary angiogram information from EMR texts. The achieved F1 score was 91.19, which is approximately 0.87 higher than the BERT-BiLSTM-CRF model.https://www.aimspress.com/article/doi/10.3934/mbe.2024180?viewType=HTMLelectronic health recordscoronary angiographygensini scoredeep learning |
| spellingShingle | Feng Li Mingfeng Jiang Hongzeng Xu Yi Chen Feng Chen Wei Nie Li Wang Data governance and Gensini score automatic calculation for coronary angiography with deep-learning-based natural language extraction Mathematical Biosciences and Engineering electronic health records coronary angiography gensini score deep learning |
| title | Data governance and Gensini score automatic calculation for coronary angiography with deep-learning-based natural language extraction |
| title_full | Data governance and Gensini score automatic calculation for coronary angiography with deep-learning-based natural language extraction |
| title_fullStr | Data governance and Gensini score automatic calculation for coronary angiography with deep-learning-based natural language extraction |
| title_full_unstemmed | Data governance and Gensini score automatic calculation for coronary angiography with deep-learning-based natural language extraction |
| title_short | Data governance and Gensini score automatic calculation for coronary angiography with deep-learning-based natural language extraction |
| title_sort | data governance and gensini score automatic calculation for coronary angiography with deep learning based natural language extraction |
| topic | electronic health records coronary angiography gensini score deep learning |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2024180?viewType=HTML |
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