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Memorias de investigación
Artículos en revistas:
Data Mining Validation of Fluconazole Breakpoints Established by the European Committee on Antimicrobial Susceptibility Testing
Año:2009
Áreas de investigación
  • Inteligencia artificial
Datos
Descripción
European Committee on Antimicrobial Susceptibility Testing (EUCAST) breakpoints classify Candida strains with a fluconazole MIC <= 2 mg/liter as susceptible, those with a fluconazole MIC of 4 mg/liter as representing intermediate susceptibility, and those with a fluconazole MIC > 4 mg/liter as resistant. Machine learning models are supported by complex statistical analyses assessing whether the results have statistical relevance. The aim of this work was to use supervised classification algorithms to analyze the clinical data used to produce EUCAST fluconazole breakpoints. Five supervised classifiers (J48, Correlation and Regression Trees [CART], OneR, Naive Bayes, and Simple Logistic) were used to analyze two cohorts of patients with oropharyngeal candidosis and candidemia. The target variable was the outcome of the infections, and the predictor variables consisted of values for the MIC or the proportion between the dose administered and the MIC of the isolate (dose/MIC). Statistical power was assessed by determining values for sensitivity and specificity, the false-positive rate, the area under the receiver operating characteristic (ROC) curve, and the Matthews correlation coefficient (MCC). CART obtained the best statistical power for a MIC > 4 mg/liter for detecting failures (sensitivity, 87%; false-positive rate, 8%; area under the ROC curve, 0.89; MCC index, 0.80). For dose/MIC determinations, the target was > 75, with a sensitivity of 91%, a false-positive rate of 10%, an area under the ROC curve of 0.90, and an MCC index of 0.80. Other classifiers gave similar breakpoints with lower statistical power. EUCAST fluconazole breakpoints have been validated by means of machine learning methods. These computer tools must be incorporated in the process for developing breakpoints to avoid researcher bias, thus enhancing the statistical power of the model.
Internacional
Si
JCR del ISI
Si
Título de la revista
ANTIMICROBIAL AGENTS AND CHEMOTHERAPY
ISSN
0066-4804
Factor de impacto JCR
4,716
Información de impacto
Volumen
53
DOI
Número de revista
7
Desde la página
2949
Hasta la página
2954
Mes
ENERO
Ranking
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Participantes
  • Participante: Albert Pahissa (Hosp Univ Valle Hebron)
  • Participante: Dolores Rodriguez Pardo (Hosp Carlos III)
  • Participante: Isabel Cuesta (Inst Salud Carlos III)
  • Participante: Fernando Laguna (Hosp Carlos III)
  • Autor: Pedro Maria Larrañaga Mugica (UPM)
  • Participante: Benito Almirante (Hosp Univ Valle Hebron)
  • Participante: Juan L. Rodriguez Tudela (Inst Salud Carlos III)
  • Autor: Maria Concepcion Bielza Lozoya (UPM)
  • Participante: Manuel Cuenca Estrella (Inst Salud Carlos III)
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Grupo de Investigación: Grupo de análisis de decisiones y estadística
  • Centro o Instituto I+D+i: Centro de tecnología Biomédica CTB
  • Grupo de Investigación: COMPUTATIONAL INTELLIGENCE GROUP
  • Departamento: Inteligencia Artificial
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