Sequential autoencoders for feature engineering and pretraining in major depressive disorder risk prediction.

ABSTRACT:

Objectives

We evaluated autoencoders as a feature engineering and pretraining technique to improve major depressive disorder (MDD) prognostic risk prediction. Autoencoders can represent temporal feature relationships not identified by aggregate features. The predictive performance of autoencoders of multiple sequential structures was evaluated as feature engineering and pretraining strategies on an array of prediction tasks and compared to a restricted Boltzmann machine (RBM) and random forests as a benchmark.

Materials and methods

We study MDD patients from Vanderbilt University Medical Center. Autoencoder models with Attention and long-short-term memory (LSTM) layers were trained to create latent representations of the input data. Predictive performance was evaluated tempo

SUBMITTER: Jones BW 

PROVIDER: S-EPMC10561992 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature