Learning to predict in-hospital mortality risk in the intensive care unit with attention-based temporal convolution network.

ABSTRACT:

Background

Dynamic prediction of patient mortality risk in the ICU with time series data is limited due to high dimensionality, uncertainty in sampling intervals, and other issues. A new deep learning method, temporal convolution network (TCN), makes it possible to deal with complex clinical time series data in ICU. We aimed to develop and validate it to predict mortality risk using time series data from MIMIC III dataset.

Methods

A total of 21,139 records of ICU stays were analysed and 17 physiological variables from the MIMIC III dataset were used to predict mortality risk. Then we compared the model performance of the attention-based TCN with that of traditional artificial intelligence (AI) methods.

Results

The area under receiver operating characteristic (AUCROC)

SUBMITTER: Chen YW 

PROVIDER: S-EPMC9034533 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature