A drug administration decision support system

Détails

ID Serval
serval:BIB_1166702CE584
Type
Actes de conférence (partie): contribution originale à la littérature scientifique, publiée à l'occasion de conférences scientifiques, dans un ouvrage de compte-rendu (proceedings), ou dans l'édition spéciale d'un journal reconnu (conference proceedings).
Collection
Publications
Titre
A drug administration decision support system
Titre de la conférence
2012 Workshop on Pharmaco-Informatics for Drug Discovery in Conjunction with 2012 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2012)
Auteur(s)
You W., Simalatsar A., Widmer N., De Micheli G.
Adresse
Philadelphia, Pennsylvania, United-States, October 4-7, 2012
ISBN
978-1-4673-2744-2
Statut éditorial
Publié
Date de publication
2012
Peer-reviewed
Oui
Pages
122-129
Langue
anglais
Résumé
Drug delivery is one of the most common clinical routines in hospitals, and is critical to patients' health and recovery. It includes a decision making process in which a medical doctor decides the amount (dose) and frequency (dose interval) on the basis of a set of available patients' feature data and the doctor's clinical experience (a priori adaptation). This process can be computerized in order to make the prescription procedure in a fast, objective, inexpensive, non-invasive and accurate way. This paper proposes a Drug Administration Decision Support System (DADSS) to help clinicians/patients with the initial dose computing. The system is based on a Support Vector Machine (SVM) algorithm for estimation of the potential drug concentration in the blood of a patient, from which a best combination of dose and dose interval is selected at the level of a DSS. The addition of the RANdom SAmple Consensus (RANSAC) technique enhances the prediction accuracy by selecting inliers for SVM modeling. Experiments are performed for the drug imatinib case study which shows more than 40% improvement in the prediction accuracy compared with previous works. An important extension to the patient features' data is also proposed in this paper.
Mots-clé
Decision Support System, RANSAC algorithm, Support Vector Machine
Création de la notice
26/12/2012 18:42
Dernière modification de la notice
20/08/2019 12:39
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