Automating the extraction of otology symptoms from clinic letters: a methodological study using natural language processing

Background Most healthcare data is in an unstructured format that requires processing to make it usable for research. Generally, this is done manually, which is both time-consuming and poorly scalable. Natural language processing (NLP) using machine learning offers a method to automate data extracti...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	BMC medical informatics and decision making Jg. 25; H. 1; S. 353 - 8
Hauptverfasser:	Joshi, Nikhil, Noor, Kawsar, Bai, Xi, Forbes, Marina, Ross, Talisa, Barrett, Liam, Dobson, Richard J. B., Schilder, Anne G. M., Mehta, Nishchay, Lilaonitkul, Watjana
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London BioMed Central 29.09.2025 BioMed Central Ltd Springer Nature B.V BMC
Schlagworte:	Annotations Automation Clinical trials Cohort analysis Collaboration Computational linguistics Data Mining - methods Datasets Dictionaries Ear Ear Diseases - diagnosis Electronic Health Records Electronic medical records Health aspects Health Informatics Hearing Hearing loss Humans Information processing Information Systems and Communication Service Language Language processing Learning algorithms Machine Learning Management of Computing and Information Systems Mechanization Medicine Medicine & Public Health Mental disorders Methods Natural language interfaces Natural Language Processing Natural language processing in medical informatics Ontology Otolaryngology Otology Patients Phenotypes Pneumonia Support vector machines Symptoms Technology application Tinnitus Unstructured data Vertigo United Kingdom Otology Natural language processing Symptoms Machine learning
ISSN:	1472-6947, 1472-6947
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!