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Stacked Denoising Autoencoders for mortality risk prediction using imbalanced clinical data.

Alhassan, Zakhriya and McGough, A. Stephen and Alshammari, Riyad and Daghstani, Tahani and Budgen, David and Al Moubayed, Noura (2018) 'Stacked Denoising Autoencoders for mortality risk prediction using imbalanced clinical data.', in 17th IEEE International Conference on Machine Learning and Applications (ICMLA) ; proceedings. , pp. 541-546.


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.

Item Type:Book chapter
Full text:(AM) Accepted Manuscript
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Publisher statement:© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Date accepted:04 September 2018
Date deposited:03 October 2018
Date of first online publication:17 January 2019
Date first made open access:No date available

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