Development and external validation of deep learning clinical prediction models using variable-length time series data.

ABSTRACT:

Objectives

To compare and externally validate popular deep learning model architectures and data transformation methods for variable-length time series data in 3 clinical tasks (clinical deterioration, severe acute kidney injury [AKI], and suspected infection).

Materials and methods

This multicenter retrospective study included admissions at 2 medical centers that spanned 2007-2022. Distinct datasets were created for each clinical task, with 1 site used for training and the other for testing. Three feature engineering methods (normalization, standardization, and piece-wise linear encoding with decision trees [PLE-DTs]) and 3 architectures (long short-term memory/gated recurrent unit [LSTM/GRU], temporal convolutional network, and time-distributed wrapper with convolutional n

SUBMITTER: Bashiri FS 

PROVIDER: S-EPMC11105134 | biostudies-literature | 2024 May

REPOSITORIES: biostudies-literature