scholarly journals Fast-DENSER: Fast Deep Evolutionary Network Structured Representation

SoftwareX ◽  
2021 ◽  
Vol 14 ◽  
pp. 100694
Author(s):  
Filipe Assunção ◽  
Nuno Lourenço ◽  
Bernardete Ribeiro ◽  
Penousal Machado
2018 ◽  
Vol 20 (1) ◽  
pp. 5-35 ◽  
Author(s):  
Filipe Assunção ◽  
Nuno Lourenço ◽  
Penousal Machado ◽  
Bernardete Ribeiro

1995 ◽  
Vol 34 (04) ◽  
pp. 310-317 ◽  
Author(s):  
B. Séné ◽  
I. de Zegher ◽  
C. Milstein ◽  
S. Errore ◽  
F de Rosis ◽  
...  

Abstract:Currently, there is no widely accepted structured representation of drug prescription. Nevertheless, a structured representation is required for entering and storing drug prescriptions avoiding free text in computerized systems, and for drug prescription reviews. Derived from part of the work of the European OPADE project, we describe an object-oriented model of drug prescription which incorporates important concepts such as the phase and triggering event concepts. This model can be used to record all drug prescriptions, including infusions, in a structured way avoiding free text. The phase concept allows the storage of sequentially ordered dosage regimens for a drug within the same prescription. The prescription triggering event concept allows recording of the administration of a drug conditional to dates, symptoms and clinical signs, medical procedures, and everyday life events. This model has been implemented within the OPADE project; the corresponding aspects of the user interface are presented to show how this model can be used in practice. Even if other new attributes may be added to the described objects, the structure of this model is suitable for general use in software which requires the entry, storage and processing of drug prescriptions.


Nature ◽  
1985 ◽  
Vol 313 (6000) ◽  
pp. 266-267 ◽  
Author(s):  
R. J. Watt

2015 ◽  
Vol 23 (4) ◽  
pp. 718-724 ◽  
Author(s):  
Vera Lúcia de Oliveira Gomes ◽  
Camila Daiane Silva ◽  
Denize Cristina de Oliveira ◽  
Daniele Ferreira Acosta ◽  
Cristiane Lopes Amarijo

AbstractObjective: to analyze the representations about domestic violence against women, among health professionals of Family Health Units.Method: qualitative study based on the Theory of Social Representations. Data were collected by means of evocations and interviews, treating them in the Ensemble de Programmes Pemettant L'Analyse des Evocations software - EVOC and content analysis.Results: nurses, physicians, nursing technicians and community health agents participated. The evocations were answered by 201 professionals and, of these, 64 were interviewed. The central core of this representation, comprised by the terms "aggression", "physical-aggression", "cowardice" and "lack of respect", which have negative connotations and were cited by interviewees. In the contrast zone, comprised by the terms "abuse", "abuse-power", "pain", "humiliation", "impunity", "suffering", "sadness" and "violence", two subgroups were identified. The first periphery contains the terms "fear", evoked most often, followed by "revolt", "low self-esteem" and "submission", and in the second periphery "acceptance" and "professional support".Conclusion: this is a structured representation since it contains conceptual, imagetic and attitudinal elements. The subgroups were comprised by professionals working in the rural area and by those who had completed their professional training course in or after 2004. These presented a representation of violence different from the representation of the general group, although all demonstrated a negative connotation of this phenomenon.


Author(s):  
Hilário Oliveira ◽  
Rinaldo Lima ◽  
João Gomes ◽  
Fred Freitas ◽  
Rafael Dueire Lins ◽  
...  

The Semantic Web, proposed by Berners-Lee, aims to make explicit the meaning of the data available on the Internet, making it possible for Web data to be processed both by people and intelligent agents. The Semantic Web requires Web data to be semantically classified and annotated with some structured representation of knowledge, such as ontologies. This chapter proposes an unsupervised, domain-independent method for extracting instances of ontological classes from unstructured data sources available on the World Wide Web. Starting with an initial set of linguistic patterns, a confidence-weighted score measure is presented integrating distinct measures and heuristics to rank candidate instances extracted from the Web. The results of several experiments are discussed achieving very encouraging results, which demonstrate the feasibility of the proposed method for automatic ontology population.


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