Assessing the severity of positive valence symptoms in initial psychiatric evaluation records: Should we use convolutional neural networks?

ABSTRACT:

Background and objective

Efficiently capturing the severity of positive valence symptoms could aid in risk stratification for adverse outcomes among patients with psychiatric disorders and identify optimal treatment strategies for patient subgroups. Motivated by the success of convolutional neural networks (CNNs) in classification tasks, we studied the application of various CNN architectures and their performance in predicting the severity of positive valence symptoms in patients with psychiatric disorders based on initial psychiatric evaluation records.

Methods

Psychiatric evaluation records contain unstructured text and semi-structured data such as question-answer pairs. For a given record, we tokenise and normalise the semi-structured content. Pre-processed tokenised wor

SUBMITTER: Dai HJ 

PROVIDER: S-EPMC6191093 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature