Drug Addiction as a Non-monotonic Process: a Multiscale Computational Model

Levy, Yariv Z.; Levy, Dino J.; Meyer, Jerrold S.; Siegelmann, Hava T.

doi:10.1007/978-3-540-92841-6_419

Cited by 2 publications

(2 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A first attempt to analyze the outputs of this theoretical model considered averages, standard deviations, integrals of the function G(t) from its average, consistency of fluctuations for different virtual subject profiles, and convergence to attractors [5]. From this analysis, it was concluded that a person with more addictive tendencies has less fluctuation and flexibility in his/her drugseeking behavior trajectory than a healthier person.…”

Section: Drug Addiction Modelmentioning

confidence: 99%

“…It was previously found that the cognitive process r(t) may strongly affect the behavioral predisposition of a virtual subject in either facilitating or impeding healthy behavior [5]. Now we consider the procedures and simulation parameters to ensure that this process will not introduce any systematic bias to the model.…”

Section: Equation-based Model Structural Analysismentioning

confidence: 99%

See 1 more Smart Citation

Identification and control of intrinsic bias in a multiscale computational model of drug addiction

Levy

Meyer

et al. 2010

Proceedings of the 2010 ACM Symposium on Applied Computing

View full text Add to dashboard Cite

Personalized medicine is rapidly evolving with the objective of providing a patient with medications based on the "use of genetic susceptibility or pharmacogenetic testing to tailor an individual's preventive care or drug therapy" [1]. It is reasonable to foresee that this domain will incorporate sources of biological knowledge other than genetics including computational modeling of diseases. For this purpose, a critical issue is how to identify and control systematic biases that may arise. In this paper, a multiscale computational model of drug addiction is presented and the interpretations of the simulated behavioral profiles of a virtual subject are discussed. These outcomes are analyzed using mathematical analytical techniques with particular attention directed to minimization of systematic biases. The simulations exemplify how a structural analysis of the model, prior to the actual simulations, may benefit the overall framework in terms of accuracy. While this paper focuses on an equation-based model for drug addiction, a similar methodology could be applied to other types of computational models for other diseases.

show abstract

Section: Drug Addiction Modelmentioning

confidence: 99%

Section: Equation-based Model Structural Analysismentioning

confidence: 99%