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

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

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 languageEnglish
Title of host publicationProceedings International Conference on Machine Learning and Data Engineering (iCMLDE 2018)
EditorsPhill Kyu Rhee, Daniel Howard, Rezaul Bashar
Place of PublicationNJ, United States
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages136-141
Number of pages6
ISBN (Electronic)9781728104041
ISBN (Print)9781728104058
DOIs
Publication statusPublished - 3 Dec 2018
EventInternational Conference on Machine Learning and Data Engineering : iCMLDE 2018 - Western Sydney University, Sydney, Australia
Duration: 3 Dec 20187 Dec 2018
http://www.icmlde.net.au/Home.aspx

Conference

ConferenceInternational Conference on Machine Learning and Data Engineering
CountryAustralia
CitySydney
Period3/12/187/12/18
Internet address

    Fingerprint

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). NJ, United States: IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/icmlde.2018.00033