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Consequently, unlocking the full potential of EHR data is contingent on the development of natural language processing (NLP) methods to automatically transform clinical text into structured clinical data that can guide clinical decisions and potentially delay or prevent disease onset.
The main aim of this study was to provide systematic evidence on the properties of text data used to train machine learning approaches to clinical NLP.
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Natural language processing software for healthcare can scan clinical text data within seconds and using machine learning models, identify what needs to be ...
Dec 21, 2022 · This paper aims to present a comprehensive review of clinical NLP for the past 15 years in the UK to identify the community, depict its evolution, analyse ...
Nov 16, 2020 · The objective of this study was to review the current methods used for developing and evaluating NLP algorithms that map clinical text fragments onto ontology ...
Clinical NLP excels at identifying and extracting specific entities within clinical text, such as medical conditions, medications, procedures, and more. This ...
This course teaches you the fundamentals of clinical natural language processing (NLP). In this course you will learn the basic linguistic principals ...
Nov 27, 2024 · Text summarization using NLP automates the process of reducing lengthy text into shorter summaries, which is useful in domains such as financial ...
Dec 26, 2022 · In this study, we develop a large clinical language model, GatorTron, using >90 billion words of text from the de-identified clinical notes of ...
Aug 13, 2024 · Natural language processing (NLP) is the key technology to use clinical narratives for health outcomes and clinical translational studies.
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