Radiological reporting has generated large quantities of digital content within the electronic
health record, which is potentially a valuable source of information for improving clinical care …

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 …

Background Concept extraction, a subdomain of natural language processing (NLP) with a
focus on extracting concepts of interest, has been adopted to computationally extract clinical …

Objective To develop a natural language processing system that solves both clinical concept
extraction and relation extraction in a unified prompt-based machine reading …

An enormous amount of clinical free-text information, such as pathology reports, progress
reports, clinical notes and discharge summaries have been collected at hospitals and …

Objective To develop soft prompt-based learning architecture for large language models
(LLMs), examine prompt-tuning using frozen/unfrozen LLMs, and assess their abilities in …

Problem: Clinical practice requires the production of a time-and resource-consuming great
amount of notes. They contain relevant information, but their secondary use is almost …

A large amount of unstructured textual data about geoscience structures and minerals is
buried in geoscience documents and is unused. Automatic keyphrase extraction provides …

Extracting clinical terms from free-text format radiology reports is a first important step toward
their secondary use. However, there is no general consensus on the kind of terms to be …

Unstructured and semi-structured radiology reports represent an underutilized trove of
information for machine learning (ML)-based clinical informatics applications, including …