Analysis of EHR Free-text Data with Supervised Deep Neural Networks

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Title: Analysis of EHR Free-text Data with Supervised Deep Neural Networks
Authors: Wallace, DuncanKechadi, Tahar
Permanent link: http://hdl.handle.net/10197/10788
Date: 29-Jul-2018
Online since: 2019-06-11T09:09:44Z
Abstract: In this paper we present an efficient supervised deep neural network architecture to classify patients based solely on free-text notes extracted from their Electronic Health Records (EHRs). In particular, a three-layer Recurrent Neural Network was used in conjunction with the aggregated EHRs of about 127,149 patients from a medical data warehouse. The result forms a key component of an application we name PANNACEA. We evaluated this neural network in the context of competing neural network architectures, comparing the performance of multilayer perceptrons, convolutional neural networks, and recurrent neural networks in relation to the dataset under investigation. We performed evaluation our program to successfully classify the suitability of these patients to the medical service offered based upon a single medical episode.
Funding Details: Science Foundation Ireland
metadata.dc.description.othersponsorship: Insight Research Centre
Type of material: Conference Publication
Start page: 245
End page: 251
Copyright (published version): 2018 CSREA Press
Keywords: BioinformaticsArtificial neural networksMining textSemi-structured data
Other versions: https://csce.ucmss.com/cr/books/2018/ConferenceReport?ConferenceKey=ICD
Language: en
Status of Item: Peer reviewed
Is part of: Stahlbock, R., Weiss, G.M., Abou-Nasr, M. (eds.). Proceedings of the 2018 International Conference on Data Science
Conference Details: CSCE'18: The 2018 World Congress in Computer Science, Computer Engineering & Applied Computing, Las Vegas, Nevada, USA, 30 July - 02 August 2018
ISBN: 1-60132-481-2
Appears in Collections:Insight Research Collection

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