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

  • Gianni D’Angelo
  • Raffaele Pilla
  • Carlo Tascini
  • Salvatore RamponeEmail author
Methodologies and Application
  • 32 Downloads

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.

Keywords

Meningitis Meningitis etiology Bacterial meningitis Viral meningitis Genetic programming Symbolic regression Decision rules Machine learning Decision tree Neural network 

Notes

Compliance with ethical standards

Conflict of interest

Authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

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Authors and Affiliations

  1. 1.Department of Law, Economics, Management and Quantitative Methods (DEMM)University of SannioBeneventoItaly
  2. 2.External PharmacySt. John of God – Fatebenefratelli HospitalBeneventoItaly
  3. 3.University of SalernoBaronissiItaly
  4. 4.First Division of Infectious DiseasesCotugno Hospital, Azienda Ospedaliera dei ColliNaplesItaly

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