Abstract Background: Natural Language Processing (NLP) is widely used to extract clinical
insights from Electronic Health Records (EHRs). However, the lack of annotated data …

Sepsis remains a major challenge internationally for healthcare systems. Its incidence is
rising due to poor public awareness and delays in its recognition and subsequent …

Negative and uncertain medical findings are frequent in radiology reports, but discriminating
them from positive findings remains challenging for information extraction. Here, we propose …

The introduction and rapid growth of the Internet of Medical Things (IoMT), a subset of the
Internet of Things (IoT) in the medical and healthcare systems, has brought numerous …

Negation is an important characteristic of language, and a major component of information
extraction from text. This subtask is of considerable importance to the biomedical domain …

Abstract Machine learning approaches hold great potential for the automated detection of
lung nodules on chest radiographs, but training algorithms requires very large amounts of …

Detecting negation and uncertainty is crucial for medical text mining applications; otherwise,
extracted information can be incorrectly identified as real or factual events. Although several …

Despite the recent success of deep neural networks in natural language processing and
other spheres of artificial intelligence, their interpretability remains a challenge. We analyze …

Motivated by the need to automate medical information extraction from free-text radiological
reports, we present a bi-directional long short-term memory (BiLSTM) neural network …

Abstract Human Phenotype Ontology (HPO) terms are increasingly used in diagnostic
settings to aid in the characterization of patient phenotypes. The HPO annotation database …