Name
Title | ||
|---|---|---|
Designing a system to extract and interpret timed causal sentences in medical reports. |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1080/0952813X.2018.1513081 | JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE |
Keywords | Field | DocType |
Causality,time,mining causal sentences,causal graphs,time constrained causal graphs | Graph,Causality,Arrow,Computer science,Plain text,Natural language processing,Artificial intelligence,Sentence,Conjecture,Internet resources | Journal |
Volume | Issue | ISSN |
31.0 | 1 | 0952-813X |
Citations | PageRank | References |
0 | 0.34 | 6 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cristina Puente | 1 | 19 | 5.60 |
| Alejandro Sobrino | 2 | 30 | 9.59 |
| José Angel Olivas | 3 | 65 | 12.87 |
| Augusto Villa Monte | 4 | 2 | 2.10 |