Personalized Drug Administrations Using Support Vector Machine

Springer Science and Business Media LLC - Tập 3 - Trang 378-393 - 2013
Wenqi You1, Alena Simalatsar1, Nicolas Widmer2, Giovanni De Micheli3,4
1School of Computer and Communication Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
2Pharmacien responsable TDM, Division de Pharmacologie etToxicologie cliniques CHUV, Hopital de Beaumont, Lausanne, Switzerland
3Institute of Electrical Engineering, EPFL, Lausanne, Switzerland
4Integrated Systems Centre, EPFL, Lausanne, Switzerland

Tóm tắt

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).

Tài liệu tham khảo

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