Extracting clinical named entity for pituitary adenomas from Chinese electronic medical records
Abstract Objective Pituitary adenomas are the most common type of pituitary disorders, which usually occur in young adults and often affect the patient’s physical development, labor capacity and fertility. Clinical free texts noted in electronic medical records (EMRs) of pituitary adenomas patients...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2022-03-01
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| Series: | BMC Medical Informatics and Decision Making |
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| Online Access: | https://doi.org/10.1186/s12911-022-01810-z |
| _version_ | 1818356679654244352 |
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| author | An Fang Jiahui Hu Wanqing Zhao Ming Feng Ji Fu Shanshan Feng Pei Lou Huiling Ren Xianlai Chen |
| author_facet | An Fang Jiahui Hu Wanqing Zhao Ming Feng Ji Fu Shanshan Feng Pei Lou Huiling Ren Xianlai Chen |
| author_sort | An Fang |
| collection | DOAJ |
| description | Abstract Objective Pituitary adenomas are the most common type of pituitary disorders, which usually occur in young adults and often affect the patient’s physical development, labor capacity and fertility. Clinical free texts noted in electronic medical records (EMRs) of pituitary adenomas patients contain abundant diagnosis and treatment information. However, this information has not been well utilized because of the challenge to extract information from unstructured clinical texts. This study aims to enable machines to intelligently process clinical information, and automatically extract clinical named entity for pituitary adenomas from Chinese EMRs. Methods The clinical corpus used in this study was from one pituitary adenomas neurosurgery treatment center of a 3A hospital in China. Four types of fine-grained texts of clinical records were selected, which included notes from present illness, past medical history, case characteristics and family history of 500 pituitary adenoma inpatients. The dictionary-based matching, conditional random fields (CRF), bidirectional long short-term memory with CRF (BiLSTM-CRF), and bidirectional encoder representations from transformers with BiLSTM-CRF (BERT-BiLSTM-CRF) were used to extract clinical entities from a Chinese EMRs corpus. A comprehensive dictionary was constructed based on open source vocabularies and a domain dictionary for pituitary adenomas to conduct the dictionary-based matching method. We selected features such as part of speech, radical, document type, and the position of characters to train the CRF-based model. Random character embeddings and the character embeddings pretrained by BERT were used respectively as the input features for the BiLSTM-CRF model and the BERT-BiLSTM-CRF model. Both strict metric and relaxed metric were used to evaluate the performance of these methods. Results Experimental results demonstrated that the deep learning and other machine learning methods were able to automatically extract clinical named entities, including symptoms, body regions, diseases, family histories, surgeries, medications, and disease courses of pituitary adenomas from Chinese EMRs. With regard to overall performance, BERT-BiLSTM-CRF has the highest strict F1 value of 91.27% and the highest relaxed F1 value of 95.57% respectively. Additional evaluations showed that BERT-BiLSTM-CRF performed best in almost all entity recognition except surgery and disease course. BiLSTM-CRF performed best in disease course entity recognition, and performed as well as the CRF model for part of speech, radical and document type features, with both strict and relaxed F1 value reaching 96.48%. The CRF model with part of speech, radical and document type features performed best in surgery entity recognition with relaxed F1 value of 95.29%. Conclusions In this study, we conducted four entity recognition methods for pituitary adenomas based on Chinese EMRs. It demonstrates that the deep learning methods can effectively extract various types of clinical entities with satisfying performance. This study contributed to the clinical named entity extraction from Chinese neurosurgical EMRs. The findings could also assist in information extraction in other Chinese medical texts. |
| first_indexed | 2024-12-13T20:01:03Z |
| format | Article |
| id | doaj.art-e63155918cd947649421785ba596ff89 |
| institution | Directory Open Access Journal |
| issn | 1472-6947 |
| language | English |
| last_indexed | 2024-12-13T20:01:03Z |
| publishDate | 2022-03-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Informatics and Decision Making |
| spelling | doaj.art-e63155918cd947649421785ba596ff892022-12-21T23:33:12ZengBMCBMC Medical Informatics and Decision Making1472-69472022-03-0122111410.1186/s12911-022-01810-zExtracting clinical named entity for pituitary adenomas from Chinese electronic medical recordsAn Fang0Jiahui Hu1Wanqing Zhao2Ming Feng3Ji Fu4Shanshan Feng5Pei Lou6Huiling Ren7Xianlai Chen8Life Science College, Central South UniversityInstitute of Medical Information, Chinese Academy of Medical SciencesInstitute of Medical Information, Chinese Academy of Medical SciencesDongcheng District, Peking Union Medical College HospitalDongcheng District, Peking Union Medical College HospitalDongcheng District, Peking Union Medical College HospitalInstitute of Medical Information, Chinese Academy of Medical SciencesInstitute of Medical Information, Chinese Academy of Medical SciencesBig Data Institute, Central South UniversityAbstract Objective Pituitary adenomas are the most common type of pituitary disorders, which usually occur in young adults and often affect the patient’s physical development, labor capacity and fertility. Clinical free texts noted in electronic medical records (EMRs) of pituitary adenomas patients contain abundant diagnosis and treatment information. However, this information has not been well utilized because of the challenge to extract information from unstructured clinical texts. This study aims to enable machines to intelligently process clinical information, and automatically extract clinical named entity for pituitary adenomas from Chinese EMRs. Methods The clinical corpus used in this study was from one pituitary adenomas neurosurgery treatment center of a 3A hospital in China. Four types of fine-grained texts of clinical records were selected, which included notes from present illness, past medical history, case characteristics and family history of 500 pituitary adenoma inpatients. The dictionary-based matching, conditional random fields (CRF), bidirectional long short-term memory with CRF (BiLSTM-CRF), and bidirectional encoder representations from transformers with BiLSTM-CRF (BERT-BiLSTM-CRF) were used to extract clinical entities from a Chinese EMRs corpus. A comprehensive dictionary was constructed based on open source vocabularies and a domain dictionary for pituitary adenomas to conduct the dictionary-based matching method. We selected features such as part of speech, radical, document type, and the position of characters to train the CRF-based model. Random character embeddings and the character embeddings pretrained by BERT were used respectively as the input features for the BiLSTM-CRF model and the BERT-BiLSTM-CRF model. Both strict metric and relaxed metric were used to evaluate the performance of these methods. Results Experimental results demonstrated that the deep learning and other machine learning methods were able to automatically extract clinical named entities, including symptoms, body regions, diseases, family histories, surgeries, medications, and disease courses of pituitary adenomas from Chinese EMRs. With regard to overall performance, BERT-BiLSTM-CRF has the highest strict F1 value of 91.27% and the highest relaxed F1 value of 95.57% respectively. Additional evaluations showed that BERT-BiLSTM-CRF performed best in almost all entity recognition except surgery and disease course. BiLSTM-CRF performed best in disease course entity recognition, and performed as well as the CRF model for part of speech, radical and document type features, with both strict and relaxed F1 value reaching 96.48%. The CRF model with part of speech, radical and document type features performed best in surgery entity recognition with relaxed F1 value of 95.29%. Conclusions In this study, we conducted four entity recognition methods for pituitary adenomas based on Chinese EMRs. It demonstrates that the deep learning methods can effectively extract various types of clinical entities with satisfying performance. This study contributed to the clinical named entity extraction from Chinese neurosurgical EMRs. The findings could also assist in information extraction in other Chinese medical texts.https://doi.org/10.1186/s12911-022-01810-zClinical information extractionPituitary adenomasChinese electronic medical recordsClinical named entity recognitionDeep learning |
| spellingShingle | An Fang Jiahui Hu Wanqing Zhao Ming Feng Ji Fu Shanshan Feng Pei Lou Huiling Ren Xianlai Chen Extracting clinical named entity for pituitary adenomas from Chinese electronic medical records BMC Medical Informatics and Decision Making Clinical information extraction Pituitary adenomas Chinese electronic medical records Clinical named entity recognition Deep learning |
| title | Extracting clinical named entity for pituitary adenomas from Chinese electronic medical records |
| title_full | Extracting clinical named entity for pituitary adenomas from Chinese electronic medical records |
| title_fullStr | Extracting clinical named entity for pituitary adenomas from Chinese electronic medical records |
| title_full_unstemmed | Extracting clinical named entity for pituitary adenomas from Chinese electronic medical records |
| title_short | Extracting clinical named entity for pituitary adenomas from Chinese electronic medical records |
| title_sort | extracting clinical named entity for pituitary adenomas from chinese electronic medical records |
| topic | Clinical information extraction Pituitary adenomas Chinese electronic medical records Clinical named entity recognition Deep learning |
| url | https://doi.org/10.1186/s12911-022-01810-z |
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