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

Kunal Rajput; Girija Chetty; Rachel Davey

doi:10.1109/ICDMW.2019.00154

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

Kunal Rajput
, Girija Chetty
, Rachel Davey

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

7 Citations (Scopus)

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 language	English
Title of host publication	Proceedings - 19th IEEE International Conference on Data Mining Workshops, ICDMW 2019
Editors	Panagiotis Papapetrou, Xueqi Cheng, Qing He
Place of Publication	United States
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	1056-1059
Number of pages	4
ISBN (Electronic)	9781728146034
ISBN (Print)	9781728148977
DOIs	https://doi.org/10.1109/ICDMW.2019.00154
Publication status	Published - 8 Nov 2019
Event	19th IEEE International Conference on Data Mining Workshops - Beijing, China Duration: 8 Nov 2019 → 11 Nov 2019

Publication series

Name	IEEE International Conference on Data Mining Workshops, ICDMW
Volume	2019-November
ISSN (Print)	2375-9232
ISSN (Electronic)	2375-9259

Workshop

Workshop	19th IEEE International Conference on Data Mining Workshops
Abbreviated title	ICDMW 2019
Country/Territory	China
City	Beijing
Period	8/11/19 → 11/11/19

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Access to Document

10.1109/ICDMW.2019.00154

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). Article 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

Rajput, Kunal ; Chetty, Girija ; Davey, Rachel. / Risk factors identification for heart disease in unstructured dataset using deep learning approach. Proceedings - 19th IEEE International Conference on Data Mining Workshops, ICDMW 2019. editor / Panagiotis Papapetrou ; Xueqi Cheng ; Qing He. United States : IEEE, Institute of Electrical and Electronics Engineers, 2019. pp. 1056-1059 (IEEE International Conference on Data Mining Workshops, ICDMW).

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title = "Risk factors identification for heart disease in unstructured dataset using deep learning approach",

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.",

keywords = "Deep learning, Disease status, Feature extraction, LSTM, PHI, Sentence encoder, Text feature",

author = "Kunal Rajput and Girija Chetty and Rachel Davey",

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doi = "10.1109/ICDMW.2019.00154",

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booktitle = "Proceedings - 19th IEEE International Conference on Data Mining Workshops, ICDMW 2019",

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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., 8955584, IEEE International Conference on Data Mining Workshops, ICDMW, vol. 2019-November, IEEE, Institute of Electrical and Electronics Engineers, United States, pp. 1056-1059, 19th IEEE International Conference on Data Mining Workshops, Beijing, China, 8/11/19. https://doi.org/10.1109/ICDMW.2019.00154

Risk factors identification for heart disease in unstructured dataset using deep learning approach. / Rajput, Kunal; Chetty, Girija ; Davey, Rachel.
Proceedings - 19th IEEE International Conference on Data Mining Workshops, ICDMW 2019. ed. / Panagiotis Papapetrou; Xueqi Cheng; Qing He. United States: IEEE, Institute of Electrical and Electronics Engineers, 2019. p. 1056-1059 8955584 (IEEE International Conference on Data Mining Workshops, ICDMW; Vol. 2019-November).

Research output: A Conference proceeding or a Chapter in Book › Conference contribution › peer-review

TY - GEN

T1 - Risk factors identification for heart disease in unstructured dataset using deep learning approach

AU - Rajput, Kunal

AU - Chetty, Girija

AU - Davey, Rachel

PY - 2019/11/8

Y1 - 2019/11/8

N2 - 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.

AB - 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.

KW - Deep learning

KW - Disease status

KW - Feature extraction

KW - LSTM

KW - PHI

KW - Sentence encoder

KW - Text feature

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M3 - Conference contribution

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SN - 9781728148977

T3 - IEEE International Conference on Data Mining Workshops, ICDMW

SP - 1056

EP - 1059

BT - Proceedings - 19th IEEE International Conference on Data Mining Workshops, ICDMW 2019

A2 - Papapetrou, Panagiotis

A2 - Cheng, Xueqi

A2 - He, Qing

PB - IEEE, Institute of Electrical and Electronics Engineers

CY - United States

T2 - 19th IEEE International Conference on Data Mining Workshops

Y2 - 8 November 2019 through 11 November 2019

ER -

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

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

Abstract

Publication series

Workshop

UN SDGs

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