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BioNanoScience

, Volume 3, Issue 4, pp 378–393 | Cite as

Personalized Drug Administrations Using Support Vector Machine

A New Approach in Computer-Aided Dose Analysis
  • Wenqi You
  • Alena Simalatsar
  • Nicolas Widmer
  • Giovanni De Micheli
Article

Abstract

The decision-making process regarding drug dose, regularly used in everyday medical practice, is critical to patients’ health and recovery. It is a challenging process, especially for a drug with narrow therapeutic ranges, in which a medical doctor decides the quantity (dose amount) and frequency (dose interval) on the basis of a set of available patient features and doctor’s clinical experience (a priori adaptation). Computer support in drug dose administration makes the prescription procedure faster, more accurate, objective, and less expensive, with a tendency to reduce the number of invasive procedures. This paper presents an advanced integrated Drug Administration Decision Support System (DADSS) to help clinicians/patients with the dose computing. Based on a support vector machine (SVM) algorithm, enhanced with the random sample consensus technique, this system is able to predict the drug concentration values and computes the ideal dose amount and dose interval for a new patient. With an extension to combine the SVM method and the explicit analytical model, the advanced integrated DADSS system is able to compute drug concentration-to-time curves for a patient under different conditions. A feedback loop is enabled to update the curve with a new measured concentration value to make it more personalized (a posteriori adaptation).

Keywords

Drug dose computation Support vector machine Decision support system 

Notes

Acknowledgments

The authors would like to thank Carlotta Guiducci from EPFL for the help in manuscript revision and T. Buclin and V. Gotta from CHUV Hospital of Lausanne for the precious suggestions on clinical data modeling and provision with sufficient data. The research work presented in this paper is funded by the ISyPeM Project “Intelligent Integrated Systems for Personalized Medicine”, with a grant from the Swiss NanoTera.ch initiative, evaluated by the Swiss National Science Foundation.

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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Wenqi You
    • 1
  • Alena Simalatsar
    • 1
  • Nicolas Widmer
    • 2
  • Giovanni De Micheli
    • 3
    • 4
  1. 1.School of Computer and Communication SciencesÉcole Polytechnique Fédérale de LausanneLausanneSwitzerland
  2. 2.Pharmacien responsable TDM, Division de Pharmacologie etToxicologie cliniques CHUVHopital de BeaumontLausanneSwitzerland
  3. 3.Institute of Electrical EngineeringEPFLLausanneSwitzerland
  4. 4.Integrated Systems CentreEPFLLausanneSwitzerland

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