Background Automatic clinical text classification is a natural language processing (NLP)
technology that unlocks information embedded in clinical narratives. Machine learning …

Background Clinical text classification is an fundamental problem in medical natural
language processing. Existing studies have cocnventionally focused on rules or knowledge …

Objective Automated clinical phenotyping is challenging because word-based features
quickly turn it into a high-dimensional problem, in which the small, privacy-restricted, training …

Background: Clinical narratives represent the main form of communication within health
care, providing a personalized account of patient history and assessments, and offering rich …

To unlock information present in clinical description, automatic medical text classification is
highly useful in the arena of natural language processing (NLP). For medical text …

Background Discharge medical notes written by physicians contain important information
about the health condition of patients. Many deep learning algorithms have been …

The main objective of this work is to improve the quality and transparency of the medical text
classification solutions. Conventional text classification methods provide users with only a …

Objective The study sought to develop and evaluate a knowledge-based data augmentation
method to improve the performance of deep learning models for biomedical natural …

We present an approach to automatically classify clinical text at a sentence level. We are
using deep convolutional neural networks to represent complex features. We train the …

Privacy and ethical considerations limit access to large-scale clinical datasets, particularly
clinical text data, which contain extensive and diverse information and serve as the …