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 100% of sensitivity in detecting a bacterial meningitis.

A proposal for distinguishing between bacterial and viral meningitis using genetic programming and decision trees

D'Angelo G.
;
2019

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 100% of sensitivity in detecting a bacterial meningitis.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11386/4771238
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