Obesity and Co-Morbidity Detection in Clinical Text Using Deep Learning and Machine Learning Techniques

Kunal Rajput; Girija Chetty; Rachel Davey

doi:10.1109/apwconcse.2018.00017

Obesity and Co-Morbidity Detection in Clinical Text Using Deep Learning and Machine Learning Techniques

Kunal Rajput
, Girija Chetty
, Rachel Davey

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

10 Citations (Scopus)

Abstract

Nowadays medical research plays an important role of community safeguard by finding the solutions to health related problems. An early detection or Co-morbidity Detection of a disease can help both patients and doctors to act and eradicate the root cause or work on preventing further deterioration of the detected disease symptoms. One of the methods to detect the disease is by going through patients' medical history but this is time-consuming manual process which comes with an expense of error-prone analysis. Hence a need to detect the co-morbidity or the existing disease in an automated or semi-automated fashion is become a need of the hour. In this paper we have used machine learning and deep learning techniques on publically available i2b2 clinical datasets to detect the chronic disease status like obesity. Our experiments have shown promising results.

Original language	English
Title of host publication	Proceedings - 2018 5th Asia-Pacific World Congress on Computer Science and Engineering, APWC on CSE 2018
Subtitle of host publication	APWConCSE 2018
Editors	A B M Shawkat Ali, Shah Miah, Maheswara Rao Valluri
Place of Publication	Danvers, USA
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	51-56
Number of pages	6
ISBN (Electronic)	9781728113906
ISBN (Print)	9781728113906
DOIs	https://doi.org/10.1109/apwconcse.2018.00017
Publication status	Published - 10 Dec 2018
Event	5th Asia-Pacific World Congress on Computer Science and Engineering, APWC on CSE 2018 - Nadi, Fiji Duration: 10 Dec 2018 → 12 Dec 2018

Publication series

Name	Proceedings - 2018 5th Asia-Pacific World Congress on Computer Science and Engineering, APWC on CSE 2018

Conference

Conference	5th Asia-Pacific World Congress on Computer Science and Engineering, APWC on CSE 2018
Country/Territory	Fiji
City	Nadi
Period	10/12/18 → 12/12/18

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/apwconcse.2018.00017

Cite this

Rajput, K., Chetty, G., & Davey, R. (2018). Obesity and Co-Morbidity Detection in Clinical Text Using Deep Learning and Machine Learning Techniques. In A. B. M. S. Ali, S. Miah, & M. Rao Valluri (Eds.), Proceedings - 2018 5th Asia-Pacific World Congress on Computer Science and Engineering, APWC on CSE 2018: APWConCSE 2018 (pp. 51-56). Article 8853828 (Proceedings - 2018 5th Asia-Pacific World Congress on Computer Science and Engineering, APWC on CSE 2018). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/apwconcse.2018.00017

Rajput, Kunal ; Chetty, Girija ; Davey, Rachel. / Obesity and Co-Morbidity Detection in Clinical Text Using Deep Learning and Machine Learning Techniques. Proceedings - 2018 5th Asia-Pacific World Congress on Computer Science and Engineering, APWC on CSE 2018: APWConCSE 2018 . editor / A B M Shawkat Ali ; Shah Miah ; Maheswara Rao Valluri. Danvers, USA : IEEE, Institute of Electrical and Electronics Engineers, 2018. pp. 51-56 (Proceedings - 2018 5th Asia-Pacific World Congress on Computer Science and Engineering, APWC on CSE 2018).

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title = "Obesity and Co-Morbidity Detection in Clinical Text Using Deep Learning and Machine Learning Techniques",

abstract = "Nowadays medical research plays an important role of community safeguard by finding the solutions to health related problems. An early detection or Co-morbidity Detection of a disease can help both patients and doctors to act and eradicate the root cause or work on preventing further deterioration of the detected disease symptoms. One of the methods to detect the disease is by going through patients' medical history but this is time-consuming manual process which comes with an expense of error-prone analysis. Hence a need to detect the co-morbidity or the existing disease in an automated or semi-automated fashion is become a need of the hour. In this paper we have used machine learning and deep learning techniques on publically available i2b2 clinical datasets to detect the chronic disease status like obesity. Our experiments have shown promising results.",

keywords = "Clinical datasets, Deep Learning, Disease Status, Machine Learning, Multi Channel, NLP technique, Single channel",

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

note = "Publisher Copyright: {\textcopyright} 2018 IEEE. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 5th Asia-Pacific World Congress on Computer Science and Engineering, APWC on CSE 2018 ; Conference date: 10-12-2018 Through 12-12-2018",

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doi = "10.1109/apwconcse.2018.00017",

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Rajput, K, Chetty, G & Davey, R 2018, Obesity and Co-Morbidity Detection in Clinical Text Using Deep Learning and Machine Learning Techniques. in ABMS Ali, S Miah & M Rao Valluri (eds), Proceedings - 2018 5th Asia-Pacific World Congress on Computer Science and Engineering, APWC on CSE 2018: APWConCSE 2018 ., 8853828, Proceedings - 2018 5th Asia-Pacific World Congress on Computer Science and Engineering, APWC on CSE 2018, IEEE, Institute of Electrical and Electronics Engineers, Danvers, USA, pp. 51-56, 5th Asia-Pacific World Congress on Computer Science and Engineering, APWC on CSE 2018, Nadi, Fiji, 10/12/18. https://doi.org/10.1109/apwconcse.2018.00017

Obesity and Co-Morbidity Detection in Clinical Text Using Deep Learning and Machine Learning Techniques. / Rajput, Kunal; Chetty, Girija ; Davey, Rachel.
Proceedings - 2018 5th Asia-Pacific World Congress on Computer Science and Engineering, APWC on CSE 2018: APWConCSE 2018 . ed. / A B M Shawkat Ali; Shah Miah; Maheswara Rao Valluri. Danvers, USA: IEEE, Institute of Electrical and Electronics Engineers, 2018. p. 51-56 8853828 (Proceedings - 2018 5th Asia-Pacific World Congress on Computer Science and Engineering, APWC on CSE 2018).

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

TY - GEN

T1 - Obesity and Co-Morbidity Detection in Clinical Text Using Deep Learning and Machine Learning Techniques

AU - Rajput, Kunal

AU - Chetty, Girija

AU - Davey, Rachel

PY - 2018/12/10

Y1 - 2018/12/10

N2 - Nowadays medical research plays an important role of community safeguard by finding the solutions to health related problems. An early detection or Co-morbidity Detection of a disease can help both patients and doctors to act and eradicate the root cause or work on preventing further deterioration of the detected disease symptoms. One of the methods to detect the disease is by going through patients' medical history but this is time-consuming manual process which comes with an expense of error-prone analysis. Hence a need to detect the co-morbidity or the existing disease in an automated or semi-automated fashion is become a need of the hour. In this paper we have used machine learning and deep learning techniques on publically available i2b2 clinical datasets to detect the chronic disease status like obesity. Our experiments have shown promising results.

AB - Nowadays medical research plays an important role of community safeguard by finding the solutions to health related problems. An early detection or Co-morbidity Detection of a disease can help both patients and doctors to act and eradicate the root cause or work on preventing further deterioration of the detected disease symptoms. One of the methods to detect the disease is by going through patients' medical history but this is time-consuming manual process which comes with an expense of error-prone analysis. Hence a need to detect the co-morbidity or the existing disease in an automated or semi-automated fashion is become a need of the hour. In this paper we have used machine learning and deep learning techniques on publically available i2b2 clinical datasets to detect the chronic disease status like obesity. Our experiments have shown promising results.

KW - Clinical datasets

KW - Deep Learning

KW - Disease Status

KW - Machine Learning

KW - Multi Channel

KW - NLP technique

KW - Single channel

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DO - 10.1109/apwconcse.2018.00017

M3 - Conference contribution

SN - 9781728113906

T3 - Proceedings - 2018 5th Asia-Pacific World Congress on Computer Science and Engineering, APWC on CSE 2018

SP - 51

EP - 56

BT - Proceedings - 2018 5th Asia-Pacific World Congress on Computer Science and Engineering, APWC on CSE 2018

A2 - Ali, A B M Shawkat

A2 - Miah, Shah

A2 - Rao Valluri, Maheswara

PB - IEEE, Institute of Electrical and Electronics Engineers

CY - Danvers, USA

T2 - 5th Asia-Pacific World Congress on Computer Science and Engineering, APWC on CSE 2018

Y2 - 10 December 2018 through 12 December 2018

ER -

Rajput K, Chetty G , Davey R. Obesity and Co-Morbidity Detection in Clinical Text Using Deep Learning and Machine Learning Techniques. In Ali ABMS, Miah S, Rao Valluri M, editors, Proceedings - 2018 5th Asia-Pacific World Congress on Computer Science and Engineering, APWC on CSE 2018: APWConCSE 2018 . Danvers, USA: IEEE, Institute of Electrical and Electronics Engineers. 2018. p. 51-56. 8853828. (Proceedings - 2018 5th Asia-Pacific World Congress on Computer Science and Engineering, APWC on CSE 2018). doi: 10.1109/apwconcse.2018.00017

Obesity and Co-Morbidity Detection in Clinical Text Using Deep Learning and Machine Learning Techniques

Abstract

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