A proposal for distinguishing between bacterial and viral meningitis using genetic programming and decision trees
Created by W.Langdon from
gp-bibliography.bib Revision:1.7970
- @Article{dangelo:SC,
-
author = "Gianni D'Angelo and Raffaele Pilla and
Carlo Tascini and Salvatore Rampone",
-
title = "A proposal for distinguishing between bacterial and
viral meningitis using genetic programming and decision
trees",
-
journal = "Soft Computing",
-
year = "2019",
-
volume = "23",
-
number = "22",
-
pages = "11775--11791",
-
month = nov,
-
note = "On line first",
-
keywords = "genetic algorithms, genetic programming, ANN,
Meningitis, Meningitis etiology, Bacterial meningitis,
Viral meningitis, Symbolic regression, Decision rules,
Machine learning, Decision tree, Neural network",
-
ISSN = "1432-7643",
-
URL = "http://link.springer.com/article/10.1007/s00500-018-03729-y",
-
DOI = "doi:10.1007/s00500-018-03729-y",
-
size = "17 pages",
-
abstract = "Meningitis is an inflammation of the protective
membranes covering the brain and the spinal cord.
Meningitis can have different causes, and
discriminating between meningitis etiologies is still
considered a hard task, especially when some specific
clinical parameters, mostly derived from blood and
cerebrospinal fluid analysis, are not completely
available. Although less frequent than its viral
version, bacterial meningitis can be fatal, especially
when diagnosis is delayed. In addition, often
unnecessary antibiotic and/or antiviral treatments are
used as a solution, which is not cost or health
effective. In this work, we address this issue through
the use of machine learning-based methodologies. We
consider two distinct cases. In one case, we take into
account both blood and cerebrospinal parameters; in the
other, we rely exclusively on the blood data. As a
result, we have rules and formulas applicable in
clinical settings. Both results highlight that a
combination of the clinical parameters is required to
properly distinguish between the two meningitis
etiologies. The results on standard and clinical
datasets show high performance. The formulas achieve
100percent of sensitivity in detecting a bacterial
meningitis.",
- }
Genetic Programming entries for
Gianni D'Angelo
Raffaele Pilla
Carlo Tascini
Salvatore Rampone
Citations