A new efficient ALignment-driven Neural Network for Mortality Prediction from Irregular Multivariate Time Series data[Formula presented]

Nzamba Bignoumba*, Nedra Mellouli, Sadok Ben Yahia

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

The irregularity of the time interval between observations in and across the stream is a key factor that leads to a drop in performance when classical machine learning or deep learning models are used for a downstream task requiring multivariate time series. Indeed, irregular multivariate time series not only increase the rate of missing values but also lead to data sparsity, which consequently makes the data almost unleverageable and/or ineffective for models. To tackle this scorching challenge, most of the pioneering approaches apply imputation or interpolation in their core, which might lead to embedding data with noise. To especially address this irregular multivariate time series issue, we introduce, in this paper, a new deep neural network model called ALignment-driven Neural Network. The innovative idea of our model is to transform the irregular multivariate time series into pseudo-aligned (or pseudo-regular) latent values. The latter are shown as a matrix, where the coefficients are the latent values of each feature at user-defined reference time points that are evenly spaced. They are obtained through a duplication process driven by an exponential decay mechanism. The obtained output is then passed to a Recurrent Neural Network model, which is undoubtedly the must-use model for regular time series data. To show that our model added value, we looked at the Intensive Care Unit mortality prediction task. In this unit, the physiological measurements used to make decisions have a problem with time irregularity. Leveraging the publicly available MIMIC-III, we compare the performance of our model to that of flagship models. In addition, we also performed extensive ablation studies to highlight the importance of specific components in our model. Interestingly enough, whenever data is collected 24 and 48 h after a patient's admission, we outperform our pioneering competitors, i.e., +1.1% and +1.5% for the AUC score, +2.3% and +2.4% for the AUPRC score and +0.6% and +1.7% for the F1-score.

Original languageEnglish
Article number122148
JournalExpert Systems with Applications
Volume238
Number of pages20
ISSN0957-4174
DOIs
Publication statusPublished - 15. Mar 2024

Keywords

  • Aligned data
  • Deep learning
  • Electronic Health Records
  • Irregular multivariate time series

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