Risk factors identification for heart disease in unstructured dataset using deep learning approach

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

Abstract

An automatic identification of the heart disease status can give timely support to medical decision-making process. The identified key factors can expedite the prevention actions in right direction. Existing solutions to identify disease factor or current disease status is based on hybrid approach which requires significant amount of human efforts. In addition to that an information extraction and de-identification on clinical dataset performed manually is error prone, expensive, prohibitively and time consuming [1]. These drawbacks can be overcome by using deep learning approach, in this paper we have used LSTM, BiLSTM and Google's Sentence encoder for automatic disease status identification. We have used i2b2 dataset with the proposed deep learning models that have led to promising results.

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
Pages1056-1059
Number of pages4
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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  • Cite this

    Rajput, K., Chetty, G., & Davey, R. (2019). Risk factors identification for heart disease in unstructured dataset using deep learning approach. In P. Papapetrou, X. Cheng, & Q. He (Eds.), Proceedings - 19th IEEE International Conference on Data Mining Workshops, ICDMW 2019 (pp. 1056-1059). [8955584] (IEEE International Conference on Data Mining Workshops, ICDMW; Vol. 2019-November). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICDMW.2019.00154