Enhancing clinical name entity recognition based on hybrid deep learning scheme

Robert Phan, Thoai Luu, Rachel Davey, Girija Chetty

Research output: A Conference proceeding or a Chapter in BookConference contribution

Abstract

This paper describes a novel machine learning approach based on deeper and wider deep learning model, for better feature learning and latent feature discovery for the clinical name entity recognition task. The performance evaluation of the proposed framework with a benchmark clinical NLP dataset, the clinical CLEF eHealth challenge 2016 dataset, has led to promising performance, when assessed in terms of F-measure, Recall and Precision. The Hybrid CNN model with hyperparameter optimization led to an F-score 89 % for the CLEF eHealth 2016 Challenge task involving synthetic nursing handover dataset.

Original languageEnglish
Title of host publicationProceedings - 19th IEEE International Conference on Data Mining Workshops, ICDMW 2019
EditorsPanagiotis Papapetrou, Xueqi Cheng, Qing He
Place of PublicationUnited States
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages1049-1055
Number of pages7
ISBN (Electronic)9781728146034
ISBN (Print)9781728148977
DOIs
Publication statusPublished - 8 Nov 2019
Event19th IEEE International Conference on Data Mining Workshops - Beijing, China
Duration: 8 Nov 201911 Nov 2019

Publication series

NameIEEE International Conference on Data Mining Workshops, ICDMW
Volume2019-November
ISSN (Print)2375-9232
ISSN (Electronic)2375-9259

Workshop

Workshop19th IEEE International Conference on Data Mining Workshops
Abbreviated titleICDMW 2019
CountryChina
CityBeijing
Period8/11/1911/11/19

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Phan, R., Luu, T., Davey, R., & Chetty, G. (2019). Enhancing clinical name entity recognition based on hybrid deep learning scheme. In P. Papapetrou, X. Cheng, & Q. He (Eds.), Proceedings - 19th IEEE International Conference on Data Mining Workshops, ICDMW 2019 (pp. 1049-1055). [8955534] (IEEE International Conference on Data Mining Workshops, ICDMW; Vol. 2019-November). United States: IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICDMW.2019.00153