FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2092162847> ?p ?o ?g. }

• W2092162847 endingPage "1251" @default.
• W2092162847 startingPage "1233" @default.
• W2092162847 abstract "Artificial neural network (ANN) modeling was used to evaluate the pharmacokinetics of aminoglycosides (arbekacin sulfate and amikacin sulfate) in severely ill patients. The plasma level was predicted by ANN modeling using parameters related to the severity of the patient's condition and the predictive performance was shown to be better than could be achieved using multiple regression analysis. These results indicate that there is a non-linear relationship between the pharmacokinetics of aminoglycosides and the severity of the patient's condition, and this should be taken into account when determining the dose for severely ill patients. Patients whose plasma levels are likely to fall below the effective level can be identified by ANN modeling with a predictive sensitivity and specificity superior to multivariate logistic regression analysis. The predictable range should be inferred from the data structure before the modeling in order to improve the predictive performance. The volume of distribution (Vd) in the normal range was weakly predicted by ANN modeling from the patients' data. Prediction of clearance by ANN modeling was poorer than that obtained from serum creatinine concentration by linear regression analysis. These results suggest that the input-output relationship (linear or non-linear) should be taken into account in selecting the modeling method. Linear modeling can give better predictive performance for linear systems and non-linear modeling can give better predictive performance for non-linear systems. In general, the performance of ANN modeling was superior to linear modeling for PK/PD prediction. For accurate modeling, a predictable range should be inferred from the data structure before the analysis. Restriction of the predictable region, as determined from the data structure, produced an increase in prediction performance. When applying ANN modeling in clinical settings, the predictive performance and predictable region should be investigated in detail to avoid the risk of harm to severely ill patients." @default.
• W2092162847 created "2016-06-24" @default.
• W2092162847 creator A5068418042 @default.
• W2092162847 date "2003-09-01" @default.
• W2092162847 modified "2023-09-23" @default.
• W2092162847 title "Clinical application of artificial neural network (ANN) modeling to predict pharmacokinetic parameters of severely ill patients" @default.
• W2092162847 cites W101894548 @default.
• W2092162847 cites W119395235 @default.
• W2092162847 cites W123389327 @default.
• W2092162847 cites W1498436455 @default.
• W2092162847 cites W1502993165 @default.
• W2092162847 cites W1546235217 @default.
• W2092162847 cites W1575826942 @default.
• W2092162847 cites W1969131649 @default.
• W2092162847 cites W1973448749 @default.
• W2092162847 cites W1974846156 @default.
• W2092162847 cites W1982740312 @default.
• W2092162847 cites W1988034908 @default.
• W2092162847 cites W1991426267 @default.
• W2092162847 cites W2006731627 @default.
• W2092162847 cites W2011982493 @default.
• W2092162847 cites W2015577757 @default.
• W2092162847 cites W2020135355 @default.
• W2092162847 cites W2030185484 @default.
• W2092162847 cites W2031389538 @default.
• W2092162847 cites W2044734436 @default.
• W2092162847 cites W2044950525 @default.
• W2092162847 cites W2046054503 @default.
• W2092162847 cites W2054329949 @default.
• W2092162847 cites W2070683645 @default.
• W2092162847 cites W2075433722 @default.
• W2092162847 cites W2077392969 @default.
• W2092162847 cites W2082323086 @default.
• W2092162847 cites W2093799856 @default.
• W2092162847 cites W2095781728 @default.
• W2092162847 cites W2102389568 @default.
• W2092162847 cites W2110274812 @default.
• W2092162847 cites W2114685000 @default.
• W2092162847 cites W2114760676 @default.
• W2092162847 cites W2115380419 @default.
• W2092162847 cites W2117063392 @default.
• W2092162847 cites W2133295230 @default.
• W2092162847 cites W2147346895 @default.
• W2092162847 cites W2162504357 @default.
• W2092162847 cites W2169182513 @default.
• W2092162847 cites W2312715682 @default.
• W2092162847 cites W2331021314 @default.
• W2092162847 cites W2411373668 @default.
• W2092162847 cites W27918916 @default.
• W2092162847 cites W4293242440 @default.
• W2092162847 doi "https://doi.org/10.1016/s0169-409x(03)00121-2" @default.
• W2092162847 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/12954201" @default.
• W2092162847 hasPublicationYear "2003" @default.
• W2092162847 type Work @default.
• W2092162847 sameAs 2092162847 @default.
• W2092162847 citedByCount "34" @default.
• W2092162847 countsByYear W20921628472012 @default.
• W2092162847 countsByYear W20921628472013 @default.
• W2092162847 countsByYear W20921628472014 @default.
• W2092162847 countsByYear W20921628472015 @default.
• W2092162847 countsByYear W20921628472016 @default.
• W2092162847 countsByYear W20921628472017 @default.
• W2092162847 countsByYear W20921628472019 @default.
• W2092162847 countsByYear W20921628472020 @default.
• W2092162847 countsByYear W20921628472021 @default.
• W2092162847 countsByYear W20921628472022 @default.
• W2092162847 crossrefType "journal-article" @default.
• W2092162847 hasAuthorship W2092162847A5068418042 @default.
• W2092162847 hasConcept C105795698 @default.
• W2092162847 hasConcept C119857082 @default.
• W2092162847 hasConcept C127413603 @default.
• W2092162847 hasConcept C146978453 @default.
• W2092162847 hasConcept C151956035 @default.
• W2092162847 hasConcept C152877465 @default.
• W2092162847 hasConcept C163175372 @default.
• W2092162847 hasConcept C204323151 @default.
• W2092162847 hasConcept C33923547 @default.
• W2092162847 hasConcept C41008148 @default.
• W2092162847 hasConcept C45804977 @default.
• W2092162847 hasConcept C48921125 @default.
• W2092162847 hasConcept C50644808 @default.
• W2092162847 hasConcept C64946054 @default.
• W2092162847 hasConceptScore W2092162847C105795698 @default.
• W2092162847 hasConceptScore W2092162847C119857082 @default.
• W2092162847 hasConceptScore W2092162847C127413603 @default.
• W2092162847 hasConceptScore W2092162847C146978453 @default.
• W2092162847 hasConceptScore W2092162847C151956035 @default.
• W2092162847 hasConceptScore W2092162847C152877465 @default.
• W2092162847 hasConceptScore W2092162847C163175372 @default.
• W2092162847 hasConceptScore W2092162847C204323151 @default.
• W2092162847 hasConceptScore W2092162847C33923547 @default.
• W2092162847 hasConceptScore W2092162847C41008148 @default.
• W2092162847 hasConceptScore W2092162847C45804977 @default.
• W2092162847 hasConceptScore W2092162847C48921125 @default.
• W2092162847 hasConceptScore W2092162847C50644808 @default.
• W2092162847 hasConceptScore W2092162847C64946054 @default.
• W2092162847 hasIssue "9" @default.
• W2092162847 hasLocation W20921628471 @default.

• next