Objective In this systematic review, we aim to synthesize the literature on the use of natural
language processing (NLP) and text mining as they apply to symptom extraction and …

Background: Natural Language Processing (NLP) methods are increasingly being utilized to
mine knowledge from unstructured health-related texts. Recent advances in noisy text …

Objective Natural language processing (NLP) of symptoms from electronic health records
(EHRs) could contribute to the advancement of symptom science. We aim to synthesize the …

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 …

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 …

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

We followed a systematic approach based on the Preferred Reporting Items for Systematic
Reviews and Meta-Analyses to identify existing clinical natural language processing (NLP) …

Background Utilisation of routinely collected electronic health records from secondary care
offers unprecedented possibilities for medical science research but can also present …

Background Machine learning systems are part of the field of artificial intelligence that
automatically learn models from data to make better decisions. Natural language processing …

Objective As clinical text mining continues to mature, its potential as an enabling technology
for innovations in patient care and clinical research is becoming a reality. A critical part of …