Temporal information is essential for accurate understanding of medical information hidden
in electronic health record texts. In the absence of temporal information, it is even impossible …

Temporal relations are crucial in constructing a timeline over the course of clinical care,
which can help medical practitioners and researchers track the progression of diseases …

Methods Our proposed methodology trains four deep learning algorithms on an input
dataset consisting of 920 patient respiratory audio files. These audio files were recorded …

Recent years have seen particular interest in using electronic medical records (EMRs) for
secondary purposes to enhance the quality and safety of healthcare delivery. EMRs tend to …

Medical free-text records store a lot of useful information that can be exploited in developing
computer-supported medicine. However, extracting the knowledge from the unstructured text …

Developing predictive modeling in medicine requires additional features from unstructured
clinical texts. In Russia, there are no instruments for natural language processing to cope …

This study attempts to improve NERC, with focus on improving recall, in Swedish electronic
patient records through resampling strategies involving negative class undersampling and …

The disease coding task involves assigning a unique identifier from a controlled vocabulary
to each disease mentioned in a clinical document. This task is relevant since it allows …

Clinical texts, although challenging to process, are rich in valuable information, and named
entity recognition is an important element in any system designed to extract relevant …

Semantic similarity is a useful approach for comparing patient phenotypes, and holds the
potential of an effective method for exploiting text-derived phenotypes for differential …