2015
Veloso, R.; Portela, F.; Santos, M. F.; Silva, Á.; Rua, F.; Abelha, A.; Machado, J.
Using domain knowledge to improve intelligent decision support in intensive medicine: A study of bacteriological infections Proceedings Article
Em: S., Filipe J. Filipe J. Loiseau (Ed.): pp. 582-587, SciTePress, 2015, ISBN: 9789897580741, (cited By 2; Conference of 7th International Conference on Agents and Artificial Intelligence, ICAART 2015 ; Conference Date: 10 January 2015 Through 12 January 2015; Conference Code:112667).
Resumo | Links | BibTeX | Etiquetas: Antibiotics; Artificial intelligence; Bacteria; Intelligent agents; Intensive care units, Decision support systems, Decision supports; Heuristics; Infections; Intcare; Therapies
@inproceedings{Veloso2015582,
title = {Using domain knowledge to improve intelligent decision support in intensive medicine: A study of bacteriological infections},
author = {R. Veloso and F. Portela and M. F. Santos and Á. Silva and F. Rua and A. Abelha and J. Machado},
editor = {Filipe J. Filipe J. Loiseau S.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84943257423&doi=10.5220%2f0005286405820587&partnerID=40&md5=9915e188ba2224ad3a6278075d5747da},
doi = {10.5220/0005286405820587},
isbn = {9789897580741},
year = {2015},
date = {2015-01-01},
journal = {ICAART 2015 - 7th International Conference on Agents and Artificial Intelligence, Proceedings},
volume = {2},
pages = {582-587},
publisher = {SciTePress},
abstract = {Nowadays antibiotic prescription is object of study in many countries. The rate of prescription varies from country to country, without being found the reasons that justify those variations. In intensive care units the number of new infections rising each day is caused by multiple factors like inpatient length of stay, low defences of the body, chirurgical infections, among others. In order to complement the support of the decision process about which should be the most efficient antibiotic it was developed a heuristic based in domain knowledge extracted from biomedical experts. This algorithm is implemented by intelligent agents. When an alert appear on the presence of a new infection, an agent collects the microbiological results for cultures, it permits to identify the bacteria, then using the rules it searches for a role of antibiotics that can be administered to the patient, based on past results. At the end the agent presents to physicians the top-five sets and the success percentage of each antibiotic. This paper presents the approach proposed and a test with a particular bacterium using real data provided by an Intensive Care Unit.},
note = {cited By 2; Conference of 7th International Conference on Agents and Artificial Intelligence, ICAART 2015 ; Conference Date: 10 January 2015 Through 12 January 2015; Conference Code:112667},
keywords = {Antibiotics; Artificial intelligence; Bacteria; Intelligent agents; Intensive care units, Decision support systems, Decision supports; Heuristics; Infections; Intcare; Therapies},
pubstate = {published},
tppubtype = {inproceedings}
}
Nowadays antibiotic prescription is object of study in many countries. The rate of prescription varies from country to country, without being found the reasons that justify those variations. In intensive care units the number of new infections rising each day is caused by multiple factors like inpatient length of stay, low defences of the body, chirurgical infections, among others. In order to complement the support of the decision process about which should be the most efficient antibiotic it was developed a heuristic based in domain knowledge extracted from biomedical experts. This algorithm is implemented by intelligent agents. When an alert appear on the presence of a new infection, an agent collects the microbiological results for cultures, it permits to identify the bacteria, then using the rules it searches for a role of antibiotics that can be administered to the patient, based on past results. At the end the agent presents to physicians the top-five sets and the success percentage of each antibiotic. This paper presents the approach proposed and a test with a particular bacterium using real data provided by an Intensive Care Unit.