Statistical algorithms for clinical decisions and prevention of genetic-related heart disease

Abstract

Drug-induced long QT syndrome (diLQTS) can lead to seemingly healthy patients experiencing cardiac arrest, specifically Torsades de Pointes (TdP). A goal of this study is to identify genetic risk markers for diLQTS and TdP for prevention of genetic-related heart disease. We use data that were made available for general research use by National Center for Biotechnology Information, focused on subjects with a history of diLQTS or TdP after taking medication. Statistical learning algorithms were used to build a predictive model. The proposed approach can provide the likelihood of benefit for the treatment based on a set of genetic markers.

KEYWORDS:

• Decision support system
• genetic markers
• personalized medicine
• statistical learning

Additional information

Funding

Hojin Moon’s research was partially supported by the Research, Scholarship, and Creative Activity (RSCA) award from California State University, Long Beach.