title: Application of artificial neural network modelling to identify severely ill patients whose aminoglycoside concentrations are likely to fall below therapeutic concentrations.
abstract: 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.
  
  
  METHODS
  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.
  
  
  CONCLUSIONS
  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.
authors: School of Pharmaceutical Sciences, Toho University, Funabashi, Chiba, Japan. yamamura@phar.toho-u.ac.jp
authors: S
authors: S
authors: Yamamura
authors: R
authors: R
authors: Takehira
authors: K
authors: K
authors: Kawada
authors: K
authors: K
authors: Nishizawa
authors: S
authors: S
authors: Katayama
authors: M
authors: M
authors: Hirano
authors: Y
authors: Y
authors: Momose
mesh_terms: D000583:Amikacin / Q000627:therapeutic use*; D000617:Aminoglycosides / Q000097:blood* / Q000627:therapeutic use*; D000900:Anti-Bacterial Agents / Q000627:therapeutic use*; D001185:Artificial Intelligence; D003982:Dibekacin / Q000031:analogs & derivatives* / Q000627:therapeutic use*; D006801:Humans; D007362:Intensive Care Units; D016015:Logistic Models; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D012016:Reference Values; D012720:Severity of Illness Index
chemical_list: D000617:Aminoglycosides; D000900:Anti-Bacterial Agents; D003982:Dibekacin; D000583:Amikacin; C031986:arbekacin