Analysis of EHR Free-text Data with Supervised Deep Neural Networks
|Title:||Analysis of EHR Free-text Data with Supervised Deep Neural Networks||Authors:||Wallace, Duncan; Kechadi, 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:||Bioinformatics; Artificial neural networks; Mining text; Semi-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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