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Application of artificial neural network modelling to identify severely ill patients whose aminoglycoside concentrations are likely to fall below therapeutic concentrations
Author(s) -
Yamamura S.,
Takehira R.,
Kawada K.,
Nishizawa K.,
Katayama S.,
Hirano M.,
Momose Y.
Publication year - 2003
Publication title -
journal of clinical pharmacy and therapeutics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.622
H-Index - 73
eISSN - 1365-2710
pISSN - 0269-4727
DOI - 10.1046/j.0269-4727.2003.00514.x
Subject(s) - logistic regression , multivariate statistics , artificial neural network , medicine , multivariate analysis , intensive care medicine , emergency medicine , computer science , machine learning
Summary Objective: Identification of ICU patients whose concentrations are likely to fall below therapeutic concentrations using artificial neural network (ANN) modelling and individual patient physiologic data. Method: Data on indicators of disease severity and some physiologic data were collected from 89 ICU patients who received arbekacin (ABK) and 61 who received amikacin (AMK). Three‐layer ANN modelling and multivariate logistic regression analysis were used to predict the plasma concentrations of the aminoglycosides (ABK and AMK) in the severely ill patients. Results: Predictive performance analysis showed that the sensitivity and specificity of ANN modelling was superior to multivariate logistic regression analysis. For accurate modelling, a predictable range should be inferred from the data structure before the analysis. Restriction of the predictable region, based on the data structure, increased predictive performance. Conclusion: ANN analysis was superior to multivariate logistic regression analysis in predicting which patients would have plasma concentrations lower than the minimum therapeutic concentration. To improve predictive performance, the predictable range should be inferred from the data structure before prediction. When applying ANN modelling in clinical settings, the predictive performance and predictable region should be investigated in detail to avoid the risk of harm to severely ill patients.