PublicadoEl 23/11/22 por Comillas
Artículo

Designing a system to extract and interpret timed causal
sentences in medical reports

tipo de documento semantico ckh_publication

Ficheros

TETA_A_1513081_P.pdf
Tamaño 1788768
Formato Adobe PDF
Fecha de publicación 12/09/2018
Autor
Puente Águeda, Cristina
Fuente Revista: Journal of Experimental and Theoretical Artificial Intelligence, Periodo: 4, Volumen: , Número: 6, Página inicial: 1, Página final: 13
Estado info:eu-repo/semantics/publishedVersion

Resumen

Idioma es-ES
Resumen

Causal sentences are a main part of the medical explanations, providing
the causes of diseases or showing the effects of medical treatments. In
medicine, causal association is frequently related to time restrictions. So,
some drugs must be taken before or after meals, being after and
before temporary constraints. Thus, we conjecture that medical papers
include a lot of time causal sentences. Causality involves a transfer of
qualities from the cause to the effect, denoted by a directed arrow. An
arrow connecting the node cause with the node effect is a causal graph.
Causal graphs are an imagery way to show the causal dependencies that
a sentence shows using plain text. In this article, we provide several
programs to extract time causal sentences from medical Internet
resources and to convert the obtained sentences in their equivalent
causal graphs, providing an enlightening image of the relations that a
text describes, showing the cause-effect links and the temporary constraints
affecting their interpretation.

Idioma en-GB
Resumen

Causal sentences are a main part of the medical explanations, providing
the causes of diseases or showing the effects of medical treatments. In
medicine, causal association is frequently related to time restrictions. So,
some drugs must be taken before or after meals, being after and
before temporary constraints. Thus, we conjecture that medical papers
include a lot of time causal sentences. Causality involves a transfer of
qualities from the cause to the effect, denoted by a directed arrow. An
arrow connecting the node cause with the node effect is a causal graph.
Causal graphs are an imagery way to show the causal dependencies that
a sentence shows using plain text. In this article, we provide several
programs to extract time causal sentences from medical Internet
resources and to convert the obtained sentences in their equivalent
causal graphs, providing an enlightening image of the relations that a
text describes, showing the cause-effect links and the temporary constraints
affecting their interpretation.

Tipo de archivo application/pdf
Idioma en-GB
Tipo de acceso info:eu-repo/semantics/openAccess
Licencia http://creativecommons.org/licenses/by-nc-nd/3.0/es/
Fecha de modificacion 09/09/2022
Fecha de disponibilidad 04/10/2018
fecha de alta 04/10/2018

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