Disease Diagnosis Prediction of EMR Based on BiGRU-Att-CapsNetwork Model

Pin Ni; Yuming Li; Jiayi Zhu; Junkun Peng; Zhenjin Dai; Gangmin Li; Xuming Bai

doi:10.1109/BigData47090.2019.9006331

Disease Diagnosis Prediction of EMR Based on BiGRU-Att-CapsNetwork Model

Pin Ni, Yuming Li, Jiayi Zhu, Junkun Peng, Zhenjin Dai, Gangmin Li, Xuming Bai

Xi'an Jiaotong-Liverpool University

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

12 Citations (Scopus)

Abstract

Electronic Medical Records (EMR) carry a large number of diseases characteristics, history and other specific details of patients, which has great value for medical diagnosis. These data with diagnostic labels can help automated diagnostic assistant to predict disease diagnosis and provide a rapid diagnostic reference for doctors. In this study, we designed a BiGRU-Att-CapsNetwork model based on our proposed CMedBERT Chinese medical domain pre-trained language model to predict disease diagnosis in Chinese EMR. In the wide-ranging comparative experiments involving a real EMR dataset (SAHSU) and an academic evaluation task dataset (CCKS 2019), our model obtained competitive performance.

Original language	English
Title of host publication	Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019
Editors	Chaitanya Baru, Jun Huan, Latifur Khan, Xiaohua Tony Hu, Ronay Ak, Yuanyuan Tian, Roger Barga, Carlo Zaniolo, Kisung Lee, Yanfang Fanny Ye
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	6166-6168
Number of pages	3
ISBN (Electronic)	9781728108582
DOIs	https://doi.org/10.1109/BigData47090.2019.9006331
Publication status	Published - Dec 2019
Event	2019 IEEE International Conference on Big Data, Big Data 2019 - Los Angeles, United States Duration: 9 Dec 2019 → 12 Dec 2019

Publication series

Name	Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019

Conference

Conference	2019 IEEE International Conference on Big Data, Big Data 2019
Country/Territory	United States
City	Los Angeles
Period	9/12/19 → 12/12/19

Keywords

Attention
BiGRU
Capsule Network
Disease Diagnosis Prediction
EMR

Access to Document

10.1109/BigData47090.2019.9006331

Cite this

Ni, P., Li, Y., Zhu, J., Peng, J., Dai, Z., Li, G., & Bai, X. (2019). Disease Diagnosis Prediction of EMR Based on BiGRU-Att-CapsNetwork Model. In C. Baru, J. Huan, L. Khan, X. T. Hu, R. Ak, Y. Tian, R. Barga, C. Zaniolo, K. Lee, & Y. F. Ye (Eds.), Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019 (pp. 6166-6168). Article 9006331 (Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData47090.2019.9006331

Ni, Pin ; Li, Yuming ; Zhu, Jiayi et al. / Disease Diagnosis Prediction of EMR Based on BiGRU-Att-CapsNetwork Model. Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019. editor / Chaitanya Baru ; Jun Huan ; Latifur Khan ; Xiaohua Tony Hu ; Ronay Ak ; Yuanyuan Tian ; Roger Barga ; Carlo Zaniolo ; Kisung Lee ; Yanfang Fanny Ye. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 6166-6168 (Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019).

@inproceedings{53f6c62fd1494b02b09b844373b2644b,

title = "Disease Diagnosis Prediction of EMR Based on BiGRU-Att-CapsNetwork Model",

abstract = "Electronic Medical Records (EMR) carry a large number of diseases characteristics, history and other specific details of patients, which has great value for medical diagnosis. These data with diagnostic labels can help automated diagnostic assistant to predict disease diagnosis and provide a rapid diagnostic reference for doctors. In this study, we designed a BiGRU-Att-CapsNetwork model based on our proposed CMedBERT Chinese medical domain pre-trained language model to predict disease diagnosis in Chinese EMR. In the wide-ranging comparative experiments involving a real EMR dataset (SAHSU) and an academic evaluation task dataset (CCKS 2019), our model obtained competitive performance.",

keywords = "Attention, BiGRU, Capsule Network, Disease Diagnosis Prediction, EMR",

author = "Pin Ni and Yuming Li and Jiayi Zhu and Junkun Peng and Zhenjin Dai and Gangmin Li and Xuming Bai",

note = "Funding Information: This work is partially supported by the AI University Research Centre (AI-URC) through XJTLU Key Programme Special Fund (KSF-P-02) and KSF-A-17. And it is also partially supported by Suzhou Science and Technology Programme Key Industrial Technology Innovation programme with project code SYG201840. We appreciate their support and guidance. Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE International Conference on Big Data, Big Data 2019 ; Conference date: 09-12-2019 Through 12-12-2019",

year = "2019",

month = dec,

doi = "10.1109/BigData47090.2019.9006331",

language = "English",

series = "Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "6166--6168",

editor = "Chaitanya Baru and Jun Huan and Latifur Khan and Hu, {Xiaohua Tony} and Ronay Ak and Yuanyuan Tian and Roger Barga and Carlo Zaniolo and Kisung Lee and Ye, {Yanfang Fanny}",

booktitle = "Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019",

}

Ni, P, Li, Y, Zhu, J, Peng, J, Dai, Z, Li, G & Bai, X 2019, Disease Diagnosis Prediction of EMR Based on BiGRU-Att-CapsNetwork Model. in C Baru, J Huan, L Khan, XT Hu, R Ak, Y Tian, R Barga, C Zaniolo, K Lee & YF Ye (eds), Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019., 9006331, Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019, Institute of Electrical and Electronics Engineers Inc., pp. 6166-6168, 2019 IEEE International Conference on Big Data, Big Data 2019, Los Angeles, United States, 9/12/19. https://doi.org/10.1109/BigData47090.2019.9006331

Disease Diagnosis Prediction of EMR Based on BiGRU-Att-CapsNetwork Model. / Ni, Pin; Li, Yuming; Zhu, Jiayi et al.
Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019. ed. / Chaitanya Baru; Jun Huan; Latifur Khan; Xiaohua Tony Hu; Ronay Ak; Yuanyuan Tian; Roger Barga; Carlo Zaniolo; Kisung Lee; Yanfang Fanny Ye. Institute of Electrical and Electronics Engineers Inc., 2019. p. 6166-6168 9006331 (Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019).

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

TY - GEN

T1 - Disease Diagnosis Prediction of EMR Based on BiGRU-Att-CapsNetwork Model

AU - Ni, Pin

AU - Li, Yuming

AU - Zhu, Jiayi

AU - Peng, Junkun

AU - Dai, Zhenjin

AU - Li, Gangmin

AU - Bai, Xuming

N1 - Funding Information: This work is partially supported by the AI University Research Centre (AI-URC) through XJTLU Key Programme Special Fund (KSF-P-02) and KSF-A-17. And it is also partially supported by Suzhou Science and Technology Programme Key Industrial Technology Innovation programme with project code SYG201840. We appreciate their support and guidance. Publisher Copyright: © 2019 IEEE.

PY - 2019/12

Y1 - 2019/12

N2 - Electronic Medical Records (EMR) carry a large number of diseases characteristics, history and other specific details of patients, which has great value for medical diagnosis. These data with diagnostic labels can help automated diagnostic assistant to predict disease diagnosis and provide a rapid diagnostic reference for doctors. In this study, we designed a BiGRU-Att-CapsNetwork model based on our proposed CMedBERT Chinese medical domain pre-trained language model to predict disease diagnosis in Chinese EMR. In the wide-ranging comparative experiments involving a real EMR dataset (SAHSU) and an academic evaluation task dataset (CCKS 2019), our model obtained competitive performance.

AB - Electronic Medical Records (EMR) carry a large number of diseases characteristics, history and other specific details of patients, which has great value for medical diagnosis. These data with diagnostic labels can help automated diagnostic assistant to predict disease diagnosis and provide a rapid diagnostic reference for doctors. In this study, we designed a BiGRU-Att-CapsNetwork model based on our proposed CMedBERT Chinese medical domain pre-trained language model to predict disease diagnosis in Chinese EMR. In the wide-ranging comparative experiments involving a real EMR dataset (SAHSU) and an academic evaluation task dataset (CCKS 2019), our model obtained competitive performance.

KW - Attention

KW - BiGRU

KW - Capsule Network

KW - Disease Diagnosis Prediction

KW - EMR

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U2 - 10.1109/BigData47090.2019.9006331

DO - 10.1109/BigData47090.2019.9006331

M3 - Conference Proceeding

AN - SCOPUS:85081323888

T3 - Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019

SP - 6166

EP - 6168

BT - Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019

A2 - Baru, Chaitanya

A2 - Huan, Jun

A2 - Khan, Latifur

A2 - Hu, Xiaohua Tony

A2 - Ak, Ronay

A2 - Tian, Yuanyuan

A2 - Barga, Roger

A2 - Zaniolo, Carlo

A2 - Lee, Kisung

A2 - Ye, Yanfang Fanny

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2019 IEEE International Conference on Big Data, Big Data 2019

Y2 - 9 December 2019 through 12 December 2019

ER -

Ni P, Li Y, Zhu J, Peng J, Dai Z, Li G et al. Disease Diagnosis Prediction of EMR Based on BiGRU-Att-CapsNetwork Model. In Baru C, Huan J, Khan L, Hu XT, Ak R, Tian Y, Barga R, Zaniolo C, Lee K, Ye YF, editors, Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 6166-6168. 9006331. (Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019). doi: 10.1109/BigData47090.2019.9006331

Disease Diagnosis Prediction of EMR Based on BiGRU-Att-CapsNetwork Model

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