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Stacked Denoising Autoencoders for Mortality Risk Prediction Using Imbalanced Clinical Data

Alhassan, Zakhriya; McGough, A. Stephen; Alshammari, Riyad; Daghstani, Tahani; Budgen, David; Al Moubayed, Noura

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Authors

Zakhriya Alhassan

A. Stephen McGough

Riyad Alshammari

Tahani Daghstani



Abstract

Clinical data, such as evaluations, treatments, vital sign and lab test results, are usually observed and recorded in hospital systems. Making use of such data to help physicians to evaluate the mortality risk of in-hospital patients provides an invaluable source of information that can ultimately help with improving healthcare services. In particular, quick and accurate predictions of mortality can be valuable for physicians who are making decisions about interventions. In this work we introduce the use of a predictive Deep Learning model to help evaluate the mortality risk for in-hospital patients. Stacked Denoising Autoencoder (SDA) has been trained using a unique time-stamped dataset (King Abdullah International Research Center - KAIMRC) which is naturally imbalanced. The results are compared to those from common deep learning approaches, using different methods for data balancing. The proposed model demonstrated here aims to overcome the problem of imbalanced data, and outperforms common deep learning approaches with an accuracy of 77.13% for the Recall macro.

Citation

Alhassan, Z., McGough, A. S., Alshammari, R., Daghstani, T., Budgen, D., & Al Moubayed, N. (2018). Stacked Denoising Autoencoders for Mortality Risk Prediction Using Imbalanced Clinical Data. In 17th IEEE International Conference on Machine Learning and Applications (ICMLA) ; proceedings (541-546). https://doi.org/10.1109/icmla.2018.00087

Conference Name IEEE 17th International Conference on Machine Learning and Applications (ICMLA 2018).
Conference Location Orlando, Fl, USA
Start Date Dec 17, 2018
End Date Dec 20, 2018
Acceptance Date Sep 4, 2018
Online Publication Date Jan 17, 2019
Publication Date Jan 1, 2018
Deposit Date Oct 2, 2018
Publicly Available Date Oct 3, 2018
Publisher Institute of Electrical and Electronics Engineers
Pages 541-546
Book Title 17th IEEE International Conference on Machine Learning and Applications (ICMLA) ; proceedings.
DOI https://doi.org/10.1109/icmla.2018.00087

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