A comparative study on deep learning models for text classification of unstructured medical notes with various levels of class imbalance.

ABSTRACT:

Background

Discharge medical notes written by physicians contain important information about the health condition of patients. Many deep learning algorithms have been successfully applied to extract important information from unstructured medical notes data that can entail subsequent actionable results in the medical domain. This study aims to explore the model performance of various deep learning algorithms in text classification tasks on medical notes with respect to different disease class imbalance scenarios.

Methods

In this study, we employed seven artificial intelligence models, a CNN (Convolutional Neural Network), a Transformer encoder, a pretrained BERT (Bidirectional Encoder Representations from Transformers), and four typical sequence neural networks models, namel

SUBMITTER: Lu H 

PROVIDER: S-EPMC9250736 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature