A Survey on Recent Named Entity Recognition and Relationship Extraction Techniques on Clinical Texts
Significant growth in Electronic Health Records (EHR) over the last decade has provided an abundance of clinical text that is mostly unstructured and untapped. This huge amount of clinical text data has motivated the development of new information extraction and text mining techniques. Named Entity...
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| Format: | Article |
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MDPI AG
2021-09-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/18/8319 |
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| author | Priyankar Bose Sriram Srinivasan William C. Sleeman Jatinder Palta Rishabh Kapoor Preetam Ghosh |
| author_facet | Priyankar Bose Sriram Srinivasan William C. Sleeman Jatinder Palta Rishabh Kapoor Preetam Ghosh |
| author_sort | Priyankar Bose |
| collection | DOAJ |
| description | Significant growth in Electronic Health Records (EHR) over the last decade has provided an abundance of clinical text that is mostly unstructured and untapped. This huge amount of clinical text data has motivated the development of new information extraction and text mining techniques. Named Entity Recognition (NER) and Relationship Extraction (RE) are key components of information extraction tasks in the clinical domain. In this paper, we highlight the present status of clinical NER and RE techniques in detail by discussing the existing proposed NLP models for the two tasks and their performances and discuss the current challenges. Our comprehensive survey on clinical NER and RE encompass current challenges, state-of-the-art practices, and future directions in information extraction from clinical text. This is the first attempt to discuss both of these interrelated topics together in the clinical context. We identified many research articles published based on different approaches and looked at applications of these tasks. We also discuss the evaluation metrics that are used in the literature to measure the effectiveness of the two these NLP methods and future research directions. |
| first_indexed | 2024-03-10T07:55:48Z |
| format | Article |
| id | doaj.art-1b6c318e91d64b72a0c147e818a72a6d |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T07:55:48Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-1b6c318e91d64b72a0c147e818a72a6d2023-11-22T11:50:51ZengMDPI AGApplied Sciences2076-34172021-09-011118831910.3390/app11188319A Survey on Recent Named Entity Recognition and Relationship Extraction Techniques on Clinical TextsPriyankar Bose0Sriram Srinivasan1William C. Sleeman2Jatinder Palta3Rishabh Kapoor4Preetam Ghosh5Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, USADepartment of Radiation Oncology, Virginia Commonwealth University, Richmond, VA 23284, USADepartment of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, USADepartment of Radiation Oncology, Virginia Commonwealth University, Richmond, VA 23284, USADepartment of Radiation Oncology, Virginia Commonwealth University, Richmond, VA 23284, USADepartment of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, USASignificant growth in Electronic Health Records (EHR) over the last decade has provided an abundance of clinical text that is mostly unstructured and untapped. This huge amount of clinical text data has motivated the development of new information extraction and text mining techniques. Named Entity Recognition (NER) and Relationship Extraction (RE) are key components of information extraction tasks in the clinical domain. In this paper, we highlight the present status of clinical NER and RE techniques in detail by discussing the existing proposed NLP models for the two tasks and their performances and discuss the current challenges. Our comprehensive survey on clinical NER and RE encompass current challenges, state-of-the-art practices, and future directions in information extraction from clinical text. This is the first attempt to discuss both of these interrelated topics together in the clinical context. We identified many research articles published based on different approaches and looked at applications of these tasks. We also discuss the evaluation metrics that are used in the literature to measure the effectiveness of the two these NLP methods and future research directions.https://www.mdpi.com/2076-3417/11/18/8319electronic health recordsclinical textnatural language processingnamed entity recognitionrelationship extractionmachine learning |
| spellingShingle | Priyankar Bose Sriram Srinivasan William C. Sleeman Jatinder Palta Rishabh Kapoor Preetam Ghosh A Survey on Recent Named Entity Recognition and Relationship Extraction Techniques on Clinical Texts Applied Sciences electronic health records clinical text natural language processing named entity recognition relationship extraction machine learning |
| title | A Survey on Recent Named Entity Recognition and Relationship Extraction Techniques on Clinical Texts |
| title_full | A Survey on Recent Named Entity Recognition and Relationship Extraction Techniques on Clinical Texts |
| title_fullStr | A Survey on Recent Named Entity Recognition and Relationship Extraction Techniques on Clinical Texts |
| title_full_unstemmed | A Survey on Recent Named Entity Recognition and Relationship Extraction Techniques on Clinical Texts |
| title_short | A Survey on Recent Named Entity Recognition and Relationship Extraction Techniques on Clinical Texts |
| title_sort | survey on recent named entity recognition and relationship extraction techniques on clinical texts |
| topic | electronic health records clinical text natural language processing named entity recognition relationship extraction machine learning |
| url | https://www.mdpi.com/2076-3417/11/18/8319 |
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