Background: Novel approaches that complement and go beyond evidence-based medicine
are required in the domain of chronic diseases, given the growing incidence of such …

Process of information extraction (IE) is used to extract useful information from unstructured
or semi-structured data. Big data arise new challenges for IE techniques with the rapid …

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

Background Large language models (LLMs) have shown remarkable capabilities in natural
language processing (NLP), especially in domains where labeled data are scarce or …

Background Natural language processing applied to clinical text or aimed at a clinical
outcome has been thriving in recent years. This paper offers the first broad overview of …

The widespread use of electronic health records (EHR) systems in health care provides a
large amount of real-world data, leading to new areas for clinical research. Natural language …

Importance Large language models (LLMs) have potential to increase the efficiency of
information extraction from unstructured clinical notes in electronic medical records …

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 …

Background Natural language processing (NLP) based clinical decision support systems
(CDSSs) have demonstrated the ability to extract vital information from patient electronic …

PURPOSE Knowing the treatments administered to patients with cancer is important for
treatment planning and correlating treatment patterns with outcomes for personalized …