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2021 | OriginalPaper | Buchkapitel

Ensemble of Deep Learning Models for In-Hospital Mortality Prediction

verfasst von : Quang H. Nguyen, Quang V. Le

Erschienen in: Advances in Engineering Research and Application

Verlag: Springer International Publishing

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Abstract

Using machine learning in health care for supporting doctors to diagnose diseases is receiving a lot of research interest. Currently, many hospitals use Electronic Health Records (EHR) to store medical records, which is an extremely valuable data source for machine learning. This paper uses the Medical Information Mart for Intensive Care (MIMIC-III) dataset to solve the problem of predicting mortality in hospitals. We have proposed standardizing numerical attributes in two ways: normalizing using mean and variance on Training set and standardizing using mean and variance according to 48 h of each sample. Neural network architectures based on CNN models have been proposed and tested. Ensemble technique has been applied to each parameter representation type and each model type. Test results show that the ensemble method has improved the performance of the system. Test results on the Test set achieve AUROC is 0.851, AUPRC is 0.452 and min (Se, + P) is 0.459.

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Titel: Ensemble of Deep Learning Models for In-Hospital Mortality Prediction
verfasst von: Quang H. Nguyen
Quang V. Le
Verlag: Springer International Publishing
Buch: Advances in Engineering Research and Application
Print ISBN: 978-3-030-64718-6

Electronic ISBN: 978-3-030-64719-3

Copyright-Jahr: 2021
DOI: https://doi.org/10.1007/978-3-030-64719-3_44

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