Medical Named Entity Recognition (MedNER): Deep learning model for recognizing medical entities (drug, disease) from scientific texts

• Main Authors: Miah, Md Saef Ullah, Junaida, Sulaiman, Talha, Sarwar, Islam, Saima Sharleen, Rahman, Mizanur, Haque, Md Samiul
• Format: Conference or Workshop Item
• Language: English; English
• Published: Institute of Electrical and Electronics Engineers Inc. 2023
• Subjects: QA75 Electronic computers. Computer science; QA76 Computer software; T Technology (General); TA Engineering (General). Civil engineering (General)
• Online Access: 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; http://umpir.ump.edu.my/id/eprint/38752/; https://doi.org/10.1109/EUROCON56442.2023.10199075

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.