2011
Miranda, M.; Machado, J.; Abelha, A.; Neves, J.; Neves, J.
Evolutionary intelligence in agent modeling and interoperability Proceedings Article
Em: P., Corchado J. M. Preuveneers D. Novais (Ed.): pp. 253-257, 2011, ISSN: 18675662, (cited By 2).
Resumo | Links | BibTeX | Etiquetas: Agent modeling; Clinical information; Health information systems; Healthcare organizations; Runtimes; Software performance, Health care; Intelligent agents; Intelligent systems; Interoperability; Semantics; Software agents, Multi agent systems
@inproceedings{Miranda2011253,
title = {Evolutionary intelligence in agent modeling and interoperability},
author = {M. Miranda and J. Machado and A. Abelha and J. Neves and J. Neves},
editor = {Corchado J. M. Preuveneers D. Novais P.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-80052975755&doi=10.1007%2f978-3-642-19937-0_33&partnerID=40&md5=278dc339bbfa3d2f6334669b614558df},
doi = {10.1007/978-3-642-19937-0_33},
issn = {18675662},
year = {2011},
date = {2011-01-01},
journal = {Advances in Intelligent and Soft Computing},
volume = {92},
pages = {253-257},
abstract = {A healthcare organization to be tuned with the users expectations, and to act according to its goals, must be accountable for the quality, cost, and overall care of the beneficiaries. In this paper we describe a model of clinical information designed to make health information systems properly interoperable and safely computable, based on an Evolutionary Intelligence approach that generates quantified scenarios from defective knowledge. The model is a response to a number of requirements, ranging from the semantic ones to the evaluation of software performance at runtime; it is among the biggest challenges in engineering nowadays. © 2011 Springer-Verlag Berlin Heidelberg.},
note = {cited By 2},
keywords = {Agent modeling; Clinical information; Health information systems; Healthcare organizations; Runtimes; Software performance, Health care; Intelligent agents; Intelligent systems; Interoperability; Semantics; Software agents, Multi agent systems},
pubstate = {published},
tppubtype = {inproceedings}
}
A healthcare organization to be tuned with the users expectations, and to act according to its goals, must be accountable for the quality, cost, and overall care of the beneficiaries. In this paper we describe a model of clinical information designed to make health information systems properly interoperable and safely computable, based on an Evolutionary Intelligence approach that generates quantified scenarios from defective knowledge. The model is a response to a number of requirements, ranging from the semantic ones to the evaluation of software performance at runtime; it is among the biggest challenges in engineering nowadays. © 2011 Springer-Verlag Berlin Heidelberg.