EVALUATION OF SPACY AND DEEPPAVLOV LIBRARY TOOLS FOR NAMED ENTITIES RECOGNITION FROM DESCRIPTIONS OF EXAMINATION RESULTS OF PATIENTS WITH COVID-19
Relevance. Determined by the need to extract significant features from electronic medical records to automate the assessment of patients' condition. Aim. Assessing the possibility of identifying named entitie in electronic descriptions of examinations of patients with COVID-19 using the BERT mo...
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| Format: | Article |
| Language: | English |
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Tomsk Polytechnic University
2023-06-01
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| Series: | Известия Томского политехнического университета: Промышленная кибернетика |
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| Online Access: | https://indcyb.ru/journal/article/view/27/22 |
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| author | Dmitry E. Sokolovsky Vladimir N. Nekrasov Sergey A. Zemlyansky Sergey V. Axyonov |
| author_facet | Dmitry E. Sokolovsky Vladimir N. Nekrasov Sergey A. Zemlyansky Sergey V. Axyonov |
| author_sort | Dmitry E. Sokolovsky |
| collection | DOAJ |
| description | Relevance. Determined by the need to extract significant features from electronic medical records to automate the assessment of patients' condition. Aim. Assessing the possibility of identifying named entitie in electronic descriptions of examinations of patients with COVID-19 using the BERT model from the SpaCy and DeepPavlov libraries.Methods. De ep learning, statistical methods. Results and conclusions. The authors have carried out a fine-tuning study on BERT neural network models from the SpaCy and DeepPavlov libraries to annotate documents “Examination of patients by the attending physician” in order to highlight the following predictors of patient assessment: temperature, blood pressure, respiratory rate, heart rate and saturation. Configuration and evaluation of the effectiveness of the architectures was carried out based on the markup of 340 anonymized electronic medical records of patients with COVID-19, obtained using the SibMED Data Clinical Repository service. It is shown that setting up models on a number of about 150 labeled documents makes it possible to determine the specified predictors in such texts with accuracy (Precision) of 85–98% and completeness (Recall) of 77–98%, depending on the predictor. The quality metrics of the architectures from the selected libraries differed slightly. Iterative expansion of the training set as a result of the operation of models with subsequent additional tuning leads to an increase in the effectiveness of the models. |
| format | Article |
| id | doaj-art-9b2c074e73524213885e1241f942252e |
| institution | Kabale University |
| issn | 2949-5407 |
| language | English |
| publishDate | 2023-06-01 |
| publisher | Tomsk Polytechnic University |
| record_format | Article |
| series | Известия Томского политехнического университета: Промышленная кибернетика |
| spelling | doaj-art-9b2c074e73524213885e1241f942252e2025-08-20T03:27:01ZengTomsk Polytechnic UniversityИзвестия Томского политехнического университета: Промышленная кибернетика2949-54072023-06-0112465310.18799/29495407/2023/2/27EVALUATION OF SPACY AND DEEPPAVLOV LIBRARY TOOLS FOR NAMED ENTITIES RECOGNITION FROM DESCRIPTIONS OF EXAMINATION RESULTS OF PATIENTS WITH COVID-19Dmitry E. Sokolovsky0Vladimir N. Nekrasov1Sergey A. Zemlyansky2Sergey V. Axyonov3National Research Tomsk Polytechnic University, RussiaS.M. Kirov Military Medical Academy, Russia National Research Tomsk State University, RussiaNational Research Tomsk Polytechnic University, RussiaRelevance. Determined by the need to extract significant features from electronic medical records to automate the assessment of patients' condition. Aim. Assessing the possibility of identifying named entitie in electronic descriptions of examinations of patients with COVID-19 using the BERT model from the SpaCy and DeepPavlov libraries.Methods. De ep learning, statistical methods. Results and conclusions. The authors have carried out a fine-tuning study on BERT neural network models from the SpaCy and DeepPavlov libraries to annotate documents “Examination of patients by the attending physician” in order to highlight the following predictors of patient assessment: temperature, blood pressure, respiratory rate, heart rate and saturation. Configuration and evaluation of the effectiveness of the architectures was carried out based on the markup of 340 anonymized electronic medical records of patients with COVID-19, obtained using the SibMED Data Clinical Repository service. It is shown that setting up models on a number of about 150 labeled documents makes it possible to determine the specified predictors in such texts with accuracy (Precision) of 85–98% and completeness (Recall) of 77–98%, depending on the predictor. The quality metrics of the architectures from the selected libraries differed slightly. Iterative expansion of the training set as a result of the operation of models with subsequent additional tuning leads to an increase in the effectiveness of the models.https://indcyb.ru/journal/article/view/27/22deep learningnamed entity extractionbertspaсydeeppavlov |
| spellingShingle | Dmitry E. Sokolovsky Vladimir N. Nekrasov Sergey A. Zemlyansky Sergey V. Axyonov EVALUATION OF SPACY AND DEEPPAVLOV LIBRARY TOOLS FOR NAMED ENTITIES RECOGNITION FROM DESCRIPTIONS OF EXAMINATION RESULTS OF PATIENTS WITH COVID-19 Известия Томского политехнического университета: Промышленная кибернетика deep learning named entity extraction bert spaсy deeppavlov |
| title | EVALUATION OF SPACY AND DEEPPAVLOV LIBRARY TOOLS FOR NAMED ENTITIES RECOGNITION FROM DESCRIPTIONS OF EXAMINATION RESULTS OF PATIENTS WITH COVID-19 |
| title_full | EVALUATION OF SPACY AND DEEPPAVLOV LIBRARY TOOLS FOR NAMED ENTITIES RECOGNITION FROM DESCRIPTIONS OF EXAMINATION RESULTS OF PATIENTS WITH COVID-19 |
| title_fullStr | EVALUATION OF SPACY AND DEEPPAVLOV LIBRARY TOOLS FOR NAMED ENTITIES RECOGNITION FROM DESCRIPTIONS OF EXAMINATION RESULTS OF PATIENTS WITH COVID-19 |
| title_full_unstemmed | EVALUATION OF SPACY AND DEEPPAVLOV LIBRARY TOOLS FOR NAMED ENTITIES RECOGNITION FROM DESCRIPTIONS OF EXAMINATION RESULTS OF PATIENTS WITH COVID-19 |
| title_short | EVALUATION OF SPACY AND DEEPPAVLOV LIBRARY TOOLS FOR NAMED ENTITIES RECOGNITION FROM DESCRIPTIONS OF EXAMINATION RESULTS OF PATIENTS WITH COVID-19 |
| title_sort | evaluation of spacy and deeppavlov library tools for named entities recognition from descriptions of examination results of patients with covid 19 |
| topic | deep learning named entity extraction bert spaсy deeppavlov |
| url | https://indcyb.ru/journal/article/view/27/22 |
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