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A clinical decision support system for the prevention of genetic-related heart disease

Author: Lauren G Saguilig
Publisher: [Long Beach, California] : California State University, Long Beach, 2017.
Dissertation: M.S. California State University, Long Beach 2017
Series: California State University, Long Beach.; Master's thesis collection, Department of Mathematics and Statistics.
Edition/Format:   Thesis/dissertation : Document : Thesis/dissertation : eBook   Computer File : English
Summary:
Abstract: Drug-induced long QT syndrome (diLQTS) is a common adverse drug reaction characterized by rapid and erratic heart beats that may instigate fainting or seizures. The onset of diLQTS can lead to torsades de points (TdP), a specific form of abnormal heart rhythm that often leads to sudden cardiac arrest and death. This study aims to understand the genetic similarities between diLQTS and TdP to develop a  Read more...
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Details

Genre/Form: Academic theses
Material Type: Document, Thesis/dissertation, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Lauren G Saguilig
ISBN: 9781369805130 1369805136
OCLC Number: 1004753554
Description: 1 online resource (v, 37 pages) : illustrations (some color).
Series Title: California State University, Long Beach.; Master's thesis collection, Department of Mathematics and Statistics.
Responsibility: by Lauren G. Saguilig.

Abstract:

Abstract: Drug-induced long QT syndrome (diLQTS) is a common adverse drug reaction characterized by rapid and erratic heart beats that may instigate fainting or seizures. The onset of diLQTS can lead to torsades de points (TdP), a specific form of abnormal heart rhythm that often leads to sudden cardiac arrest and death. This study aims to understand the genetic similarities between diLQTS and TdP to develop a clinical decision support system (CDSS) to aide physicians in the prevention of TdP. Highly accurate classification algorithms, including random forests, shrunken centroid, and diagonal linear discriminant analysis are considered to build a prediction model for TdP. With a feasible set of markers, we accurately predict TdP classifications with an accuracy above 90%. The methodology used in this study can be extended to dealing with other biomedical high-dimensional data.

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