Zhou, Xi, Xiang, Wei, and Huang, Tao (2023) A novel neural network for improved in-hospital mortality prediction with irregular and incomplete multivariate data. Neural Networks, 167. pp. 741-750.

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Abstract

Accurate estimation of in-hospital mortality based on patients’ physiological time series data improves the performance of the clinical decision support systems and assists hospital providers in allocating resources. In practice, the data quality issues of missing values are ubiquitous in electronic health records (EHRs). Since the vital signs are usually observed with irregular temporal intervals and different sampling rates, it is challenging to predict clinical outcomes with sparse and incomplete multivariate time series. We propose an auto-regressive recurrent neural network (RNN) based model, dubbed the bi-directional recursive encoder–decoder network (BiRED), to jointly perform data imputation and mortality prediction. To capture complex patterns of medical time sequences, a 2D cross-regression with an RNN unit (2DCR-RNN) and an imputation block with an RNN unit (IB-RNN) are designed as the recurrent component of the encoder and decoder, respectively. Furthermore, a state initialization method is proposed to alleviate errors accumulated in the generated sequence. The experimental results on two real EHR datasets show that our proposed method can predict hospital mortality with high AUC scores.

Item ID:	79765
Item Type:	Article (Research - C1)
ISSN:	1879-2782
Keywords:	Irregularly sampled time serie; sMultivariate time series; Missing values; Neural network; Machine learning; Precision medicine
Copyright Information:	© 2023 Published by Elsevier Ltd.
Date Deposited:	15 Aug 2023 01:50
FoR Codes:	46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461103 Deep learning @ 50% 46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461104 Neural networks @ 50%
SEO Codes:	22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220403 Artificial intelligence @ 60% 20 HEALTH > 2002 Evaluation of health and support services > 200202 Evaluation of health outcomes @ 40%
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