Citation

Kuttaiyapillai, Dhanasekaran and Madasamy, Anand and Ayyavu, Shobanadevi and Sayeed, Md. Shohel (2024) Clinical named entity extraction for extracting information from medical data. Indonesian Journal of Electrical Engineering and Computer Science, 35 (3). p. 1722. ISSN 2502-4752

Text Clinical named entity extraction for extracting information from medical data _ Kuttaiyapillai _ Indonesian Journal of Electrical Engineering and Computer Science.pdf - Published Version Restricted to Repository staff only Download (3MB)

Abstract

Clinical named entity extraction(NER)based on deep learning gained much attention among researchers and data analysts. This paper proposes a NERapproach to extract valuable Parkinson’s disease-related information. To develop an effective NERmethod and to handle problems in disease data analytics, a unique NERtechnique applies a “recognize-map-extract (RME)” mechanism and aims to deal with complex relationships present in the data. Due to the fast-growing medical data, there is a challenge in the development of suitable deep-learning methods for NER. Furthermore, the traditional machine learning approaches rely on the time-consuming process of creating corpora and cannot extract information for specific needs and locations in certain situations. This paper presents a clinical NERapproach based on a convolutional neural network (CNN) for better use of specific features around medical entities and analyzes the performance of the proposed approach through fine-tuning NERwith effective pre-training on the BC5CDR dataset. The proposed method uses annotation of entities for various medical concepts. The second stage develops a clinically NERmethod. This proposed method shows interesting results on the performance measures achieving a precision of 92.57%, recall of 92.22%, and F1-measure of 91.6%

Item Type:	Article
Uncontrolled Keywords:	Clinical data analysis, deep learning
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines R Medicine > RA Public aspects of medicine > RA421-790.95 Public health. Hygiene. Preventive medicine
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	31 Jul 2024 04:07
Last Modified:	31 Jul 2024 04:07
URII:	http://shdl.mmu.edu.my/id/eprint/12680

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