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

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.