Research ArticlesApplication of Neural Networks to Pharmacodynamics
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New application of bioelectrical impedance analysis by the back propagation artificial neural network mathematically predictive model of tissue composition in the lower limbs of elderly people
2012, International Journal of GerontologyCitation Excerpt :Application of linear regression analysis on single independent variables and single dependant variables is suitable; however, multiple parameters, especially when interactions exist between the parameters, will result in errors in the predictive equation. Besides the most popular method for outcome prediction, artificial neural networks (ANNs)26–33, there are other mathematical methods such as logistic regression34, disscriminant analysis and recursive partitioning35. The ANN model has performed well, with greater precision and accuracy in the prediction of intercellular fluid and TBW in patients with chronic hemodialysis than that of the linear regression model36.
Using neural networks to determine the contribution of danshensu to its multiple cardiovascular activities in acute myocardial infarction rats
2011, Journal of EthnopharmacologyCitation Excerpt :It has been shown that BP NN could approximate the PK and PD profiles generated from simulations of several structural PK/PD models (Gobburu and Chen, 1996). Furthermore, BP NN has been successfully applied in the bioequivalence study (Opara et al., 1999), population pharmacokinetics (Chow et al., 1997), clinical pharmacology (Brier and Aronoff, 1996; Urquidi-Macdonald et al., 2004; Mager et al., 2005) and PK/PD modeling (Veng-Pedersen and Modi, 1993; Haidar et al., 2002). Ischemic heart disease has been regarded as a complex disorder associated with numerous risk factors such as hypertension, diabetes and atherosclerosis (Ferdinandy et al., 2007).
Analysis of pellet properties with use of artificial neural networks
2010, European Journal of Pharmaceutical SciencesCitation Excerpt :Superior abilities of ANNs as empirical modeling tools have attracted attention of scientists in the pharmaceutical field as well. ANNs were used as predictive models in pharmacokinetics and pharmacodynamics (Brier and Żurada, 1995; Brier and Aronoff, 1996; Chow et al., 1997; Hussain et al., 1993; Veng-Pedersen and Modi, 1992, 1993), in vitro–in vivo correlation (IVIVC) development (Dowell et al., 1999), and pharmaceutical technology as well (Hussain et al., 1994). Hussain et al. (1991) were the first to introduce ANNs to the field of pharmaceutical technology, pointing to the possible advantages of guided search for the optimal pharmaceutical formulation.
In vitro-in vivo correlations of self-emulsifying drug delivery systems combining the dynamic lipolysis model and neuro-fuzzy networks
2008, European Journal of Pharmaceutics and BiopharmaceuticsQuantitative structure-pharmacokinetic/pharmacodynamic relationships
2006, Advanced Drug Delivery ReviewsMapping the dose-effect relationship of orbofiban from sparse data with an artificial neural network
2005, Journal of Pharmaceutical Sciences