Medical Named Entity Recognition (MedNER): Deep learning model for recognizing medical entities (drug, disease) from scientific texts
Medical Named Entity Recognition (MedNER) is an indispensable task in biomedical text mining. NER aims to recognize and categorize named entities in scientific literature, such as genes, proteins, diseases, and medications. This work is difficult due to the complexity of scientific language and the...
| Main Authors: | , , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English English |
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Institute of Electrical and Electronics Engineers Inc.
2023
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/38752/ http://umpir.ump.edu.my/id/eprint/38752/1/Medical%20named%20entity%20recognition%20%28MedNER%29_A%20deep%20learning%20model.pdf http://umpir.ump.edu.my/id/eprint/38752/2/Medical%20Named%20Entity%20Recognition%20%28MedNER%29_Deep%20learning%20model%20for%20recognizing%20medical%20entities%20%28drug%2C%20disease%29%20from%20scientific%20texts_ABS.pdf |
| _version_ | 1848825588648771584 |
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| author | Miah, Md Saef Ullah Junaida, Sulaiman Talha, Sarwar Islam, Saima Sharleen Rahman, Mizanur Haque, Md Samiul |
| author_facet | Miah, Md Saef Ullah Junaida, Sulaiman Talha, Sarwar Islam, Saima Sharleen Rahman, Mizanur Haque, Md Samiul |
| author_sort | Miah, Md Saef Ullah |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Medical Named Entity Recognition (MedNER) is an indispensable task in biomedical text mining. NER aims to recognize and categorize named entities in scientific literature, such as genes, proteins, diseases, and medications. This work is difficult due to the complexity of scientific language and the abundance of available material in the biomedical sector. Using domain-specific embedding and Bi-LSTM, we propose a novel NER model that employs deep learning approaches to improve the performance of NER on scientific publications. Our model gets 98% F1-score on a curated data-set of Covid-related scientific publications published in multiple web of science and pubmed indexed journals, significantly outperforming previous approaches deployed on the same data-set. Our findings illustrate the efficacy of our approach in reliably recognizing and classifying named entities (drug and disease) in scientific literature, opening the way for future developments in biomedical text mining. |
| first_indexed | 2025-11-15T03:31:19Z |
| format | Conference or Workshop Item |
| id | ump-38752 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-15T03:31:19Z |
| publishDate | 2023 |
| publisher | Institute of Electrical and Electronics Engineers Inc. |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-387522023-11-06T04:28:54Z http://umpir.ump.edu.my/id/eprint/38752/ Medical Named Entity Recognition (MedNER): Deep learning model for recognizing medical entities (drug, disease) from scientific texts Miah, Md Saef Ullah Junaida, Sulaiman Talha, Sarwar Islam, Saima Sharleen Rahman, Mizanur Haque, Md Samiul QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) Medical Named Entity Recognition (MedNER) is an indispensable task in biomedical text mining. NER aims to recognize and categorize named entities in scientific literature, such as genes, proteins, diseases, and medications. This work is difficult due to the complexity of scientific language and the abundance of available material in the biomedical sector. Using domain-specific embedding and Bi-LSTM, we propose a novel NER model that employs deep learning approaches to improve the performance of NER on scientific publications. Our model gets 98% F1-score on a curated data-set of Covid-related scientific publications published in multiple web of science and pubmed indexed journals, significantly outperforming previous approaches deployed on the same data-set. Our findings illustrate the efficacy of our approach in reliably recognizing and classifying named entities (drug and disease) in scientific literature, opening the way for future developments in biomedical text mining. Institute of Electrical and Electronics Engineers Inc. 2023 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/38752/1/Medical%20named%20entity%20recognition%20%28MedNER%29_A%20deep%20learning%20model.pdf pdf en http://umpir.ump.edu.my/id/eprint/38752/2/Medical%20Named%20Entity%20Recognition%20%28MedNER%29_Deep%20learning%20model%20for%20recognizing%20medical%20entities%20%28drug%2C%20disease%29%20from%20scientific%20texts_ABS.pdf Miah, Md Saef Ullah and Junaida, Sulaiman and Talha, Sarwar and Islam, Saima Sharleen and Rahman, Mizanur and Haque, Md Samiul (2023) Medical Named Entity Recognition (MedNER): Deep learning model for recognizing medical entities (drug, disease) from scientific texts. In: EUROCON 2023 - 20th International Conference on Smart Technologies, Proceedings; 20th International Conference on Smart Technologies, EUROCON 2023 , 6-8 July 2023 , Torino. pp. 158-162.. ISBN 978-166546397-3 (Published) https://doi.org/10.1109/EUROCON56442.2023.10199075 |
| spellingShingle | QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) Miah, Md Saef Ullah Junaida, Sulaiman Talha, Sarwar Islam, Saima Sharleen Rahman, Mizanur Haque, Md Samiul Medical Named Entity Recognition (MedNER): Deep learning model for recognizing medical entities (drug, disease) from scientific texts |
| title | Medical Named Entity Recognition (MedNER): Deep learning model for recognizing medical entities (drug, disease) from scientific texts |
| title_full | Medical Named Entity Recognition (MedNER): Deep learning model for recognizing medical entities (drug, disease) from scientific texts |
| title_fullStr | Medical Named Entity Recognition (MedNER): Deep learning model for recognizing medical entities (drug, disease) from scientific texts |
| title_full_unstemmed | Medical Named Entity Recognition (MedNER): Deep learning model for recognizing medical entities (drug, disease) from scientific texts |
| title_short | Medical Named Entity Recognition (MedNER): Deep learning model for recognizing medical entities (drug, disease) from scientific texts |
| title_sort | medical named entity recognition (medner): deep learning model for recognizing medical entities (drug, disease) from scientific texts |
| topic | QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) |
| url | http://umpir.ump.edu.my/id/eprint/38752/ http://umpir.ump.edu.my/id/eprint/38752/ http://umpir.ump.edu.my/id/eprint/38752/1/Medical%20named%20entity%20recognition%20%28MedNER%29_A%20deep%20learning%20model.pdf http://umpir.ump.edu.my/id/eprint/38752/2/Medical%20Named%20Entity%20Recognition%20%28MedNER%29_Deep%20learning%20model%20for%20recognizing%20medical%20entities%20%28drug%2C%20disease%29%20from%20scientific%20texts_ABS.pdf |