Basic Artificial Intelligence Techniques: Natural Language Processing of Radiology Reports.


Journal

Radiologic clinics of North America
ISSN: 1557-8275
Titre abrégé: Radiol Clin North Am
Pays: United States
ID NLM: 0123703

Informations de publication

Date de publication:
Nov 2021
Historique:
entrez: 25 10 2021
pubmed: 26 10 2021
medline: 29 10 2021
Statut: ppublish

Résumé

Natural language processing (NLP) is a subfield of computer science and linguistics that can be applied to extract meaningful information from radiology reports. Symbolic NLP is rule based and well suited to problems that can be explicitly defined by a set of rules. Statistical NLP is better situated to problems that cannot be well defined and requires annotated or labeled examples from which machine learning algorithms can infer the rules. Both symbolic and statistical NLP have found success in a variety of radiology use cases. More recently, deep learning approaches, including transformers, have gained traction and demonstrated good performance.

Identifiants

pubmed: 34689877
pii: S0033-8389(21)00077-4
doi: 10.1016/j.rcl.2021.06.003
pii:
doi:

Types de publication

Journal Article Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

919-931

Informations de copyright

Published by Elsevier Inc.

Auteurs

Jackson Steinkamp (J)

Department of Medicine, Hospital of the University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, USA.

Tessa S Cook (TS)

Perelman School of Medicine at the University of Pennsylvania, 3400 Spruce Street, 1 Silverstein Radiology, Philadelphia, PA 19104, USA. Electronic address: Tessa.Cook@pennmedicine.upenn.edu.

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Classifications MeSH