Designing a system to extract and interpret timed causal sentences in medical reports
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Date
12/09/2018Author
Estado
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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. 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.
Designing a system to extract and interpret timed causal
sentences in medical reports
Tipo de Actividad
Artículos en revistasISSN
0952-813XPalabras Clave
Causalidad, causalidad temporal, relaciones causalesCausality; time; mining causal sentences; causal graphs; time constrained causal graphs