TA-RNN: an attention-based time-aware recurrent neural network architecture for electronic health records.

ABSTRACT:

Motivation

Electronic health records (EHRs) represent a comprehensive resource of a patient's medical history. EHRs are essential for utilizing advanced technologies such as deep learning (DL), enabling healthcare providers to analyze extensive data, extract valuable insights, and make precise and data-driven clinical decisions. DL methods such as recurrent neural networks (RNN) have been utilized to analyze EHR to model disease progression and predict diagnosis. However, these methods do not address some inherent irregularities in EHR data such as irregular time intervals between clinical visits. Furthermore, most DL models are not interpretable. In this study, we propose two interpretable DL architectures based on RNN, namely time-aware RNN (TA-RNN) and TA-RNN-autoencoder (TA-RNN

SUBMITTER: Al Olaimat M 

PROVIDER: S-EPMC11211851 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature