Performance Analysis of Deep Neural Models for Automatic Identification of Disease Status

Kunal Rajput; Girija Chetty; Rachel Davey

doi:10.1109/icmlde.2018.00033

Performance Analysis of Deep Neural Models for Automatic Identification of Disease Status

Kunal Rajput, Girija Chetty, Rachel Davey

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

3 Citations (Scopus)

Abstract

An early detection of the disease status can play a vital role, as timely action can save patient's life. A disease status of the patient is a type of PHI (Protected Health Infonnation).In general the process ofidentifying the PHI is done manually on the structured dataset, this activity is prohibitively expensive, time consuming and error prone.In order to overcome the known drawback of the manual identification system an implementation of automated personal health information identification like disease status is required. In our work an effort has been done to build a model with a novel approach based on deep neural models for automatic identification of disease status (one of the PHIs) from free text clinical records. The experimental evaluation of the proposed models on two different datasets from i2b2 Challenge tasks, show the merit ofthe proposed scheme.

Original language	English
Title of host publication	Proceedings International Conference on Machine Learning and Data Engineering (iCMLDE 2018)
Editors	Phill Kyu Rhee, Daniel Howard, Rezaul Bashar
Place of Publication	NJ, United States
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	136-141
Number of pages	6
ISBN (Electronic)	9781728104041
ISBN (Print)	9781728104058
DOIs	https://doi.org/10.1109/icmlde.2018.00033
Publication status	Published - 3 Dec 2018
Event	International Conference on Machine Learning and Data Engineering 2018: iCMLDE 2018 - Western Sydney University, Sydney, Australia Duration: 3 Dec 2018 → 7 Dec 2018 http://www.icmlde.net.au/Home.aspx

Publication series

Name	Proceedings - International Conference on Machine Learning and Data Engineering, iCMLDE 2018

Conference

Conference	International Conference on Machine Learning and Data Engineering 2018
Country/Territory	Australia
City	Sydney
Period	3/12/18 → 7/12/18
Internet address	http://www.icmlde.net.au/Home.aspx

Access to Document

10.1109/icmlde.2018.00033

Cite this

Rajput, K., Chetty, G., & Davey, R. (2018). Performance Analysis of Deep Neural Models for Automatic Identification of Disease Status. In P. K. Rhee, D. Howard, & R. Bashar (Eds.), Proceedings International Conference on Machine Learning and Data Engineering (iCMLDE 2018) (pp. 136-141). [8614016] (Proceedings - International Conference on Machine Learning and Data Engineering, iCMLDE 2018). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/icmlde.2018.00033

Rajput, Kunal ; Chetty, Girija ; Davey, Rachel. / Performance Analysis of Deep Neural Models for Automatic Identification of Disease Status. Proceedings International Conference on Machine Learning and Data Engineering (iCMLDE 2018). editor / Phill Kyu Rhee ; Daniel Howard ; Rezaul Bashar. NJ, United States : IEEE, Institute of Electrical and Electronics Engineers, 2018. pp. 136-141 (Proceedings - International Conference on Machine Learning and Data Engineering, iCMLDE 2018).

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title = "Performance Analysis of Deep Neural Models for Automatic Identification of Disease Status",

abstract = "An early detection of the disease status can play a vital role, as timely action can save patient's life. A disease status of the patient is a type of PHI (Protected Health Infonnation).In general the process ofidentifying the PHI is done manually on the structured dataset, this activity is prohibitively expensive, time consuming and error prone.In order to overcome the known drawback of the manual identification system an implementation of automated personal health information identification like disease status is required. In our work an effort has been done to build a model with a novel approach based on deep neural models for automatic identification of disease status (one of the PHIs) from free text clinical records. The experimental evaluation of the proposed models on two different datasets from i2b2 Challenge tasks, show the merit ofthe proposed scheme.",

keywords = "Deep neural model, Disease Status, Protected Health Infonnation",

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

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doi = "10.1109/icmlde.2018.00033",

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Rajput, K, Chetty, G & Davey, R 2018, Performance Analysis of Deep Neural Models for Automatic Identification of Disease Status. in PK Rhee, D Howard & R Bashar (eds), Proceedings International Conference on Machine Learning and Data Engineering (iCMLDE 2018)., 8614016, Proceedings - International Conference on Machine Learning and Data Engineering, iCMLDE 2018, IEEE, Institute of Electrical and Electronics Engineers, NJ, United States, pp. 136-141, International Conference on Machine Learning and Data Engineering 2018, Sydney, New South Wales, Australia, 3/12/18. https://doi.org/10.1109/icmlde.2018.00033

Performance Analysis of Deep Neural Models for Automatic Identification of Disease Status. / Rajput, Kunal; Chetty, Girija ; Davey, Rachel.
Proceedings International Conference on Machine Learning and Data Engineering (iCMLDE 2018). ed. / Phill Kyu Rhee; Daniel Howard; Rezaul Bashar. NJ, United States: IEEE, Institute of Electrical and Electronics Engineers, 2018. p. 136-141 8614016 (Proceedings - International Conference on Machine Learning and Data Engineering, iCMLDE 2018).

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

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AU - Chetty, Girija

AU - Davey, Rachel

PY - 2018/12/3

Y1 - 2018/12/3

N2 - An early detection of the disease status can play a vital role, as timely action can save patient's life. A disease status of the patient is a type of PHI (Protected Health Infonnation).In general the process ofidentifying the PHI is done manually on the structured dataset, this activity is prohibitively expensive, time consuming and error prone.In order to overcome the known drawback of the manual identification system an implementation of automated personal health information identification like disease status is required. In our work an effort has been done to build a model with a novel approach based on deep neural models for automatic identification of disease status (one of the PHIs) from free text clinical records. The experimental evaluation of the proposed models on two different datasets from i2b2 Challenge tasks, show the merit ofthe proposed scheme.

AB - An early detection of the disease status can play a vital role, as timely action can save patient's life. A disease status of the patient is a type of PHI (Protected Health Infonnation).In general the process ofidentifying the PHI is done manually on the structured dataset, this activity is prohibitively expensive, time consuming and error prone.In order to overcome the known drawback of the manual identification system an implementation of automated personal health information identification like disease status is required. In our work an effort has been done to build a model with a novel approach based on deep neural models for automatic identification of disease status (one of the PHIs) from free text clinical records. The experimental evaluation of the proposed models on two different datasets from i2b2 Challenge tasks, show the merit ofthe proposed scheme.

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KW - Disease Status

KW - Protected Health Infonnation

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T3 - Proceedings - International Conference on Machine Learning and Data Engineering, iCMLDE 2018

SP - 136

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BT - Proceedings International Conference on Machine Learning and Data Engineering (iCMLDE 2018)

A2 - Rhee, Phill Kyu

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A2 - Bashar, Rezaul

PB - IEEE, Institute of Electrical and Electronics Engineers

CY - NJ, United States

T2 - International Conference on Machine Learning and Data Engineering 2018

Y2 - 3 December 2018 through 7 December 2018

ER -

Rajput K, Chetty G , Davey R. Performance Analysis of Deep Neural Models for Automatic Identification of Disease Status. In Rhee PK, Howard D, Bashar R, editors, Proceedings International Conference on Machine Learning and Data Engineering (iCMLDE 2018). NJ, United States: IEEE, Institute of Electrical and Electronics Engineers. 2018. p. 136-141. 8614016. (Proceedings - International Conference on Machine Learning and Data Engineering, iCMLDE 2018). doi: 10.1109/icmlde.2018.00033

Performance Analysis of Deep Neural Models for Automatic Identification of Disease Status

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