Much of the knowledge and information needed for enabling high-quality clinical research is
stored in free-text format. Natural language processing (NLP) has been used to extract …

Natural language processing (NLP) is a set of automated methods to organise and evaluate
the information contained in unstructured clinical notes, which are a rich source of real-world …

Objective Natural language processing (NLP) combined with machine learning (ML)
techniques are increasingly used to process unstructured/free-text patient-reported outcome …

Clinical trials often fail to recruit an adequate number of appropriate patients. Identifying
eligible trial participants is resource-intensive when relying on manual review of clinical …

With increasing digitization of healthcare, real-world data (RWD) are available in greater
quantity and scope than ever before. Since the 2016 United States 21st Century Cures Act …

Background Long Covid is a widely recognised consequence of COVID-19 infection, but
little is known about the burden of symptoms that patients present with in primary care, as …

With the advances in technology and data science, machine learning (ML) is being rapidly
adopted by the health care sector. However, there is a lack of literature addressing the …

Objectives Better indicators from affordable, sustainable data sources are needed to monitor
population burden of musculoskeletal conditions. We propose five indicators of …

Electronic health records naturally contain most of the medical information in the form of
doctor's notes as unstructured or semi-structured texts. Current deep learning text analysis …

Background: The analysis of clinical free text from patient records for research has potential
to contribute to the medical evidence base but access to clinical free text is frequently denied …