Abstract
Abstract Class imbalance can present a major issue in time-to-event analyses for instances where the number of individuals diagnosed with a disease are far outnumbered by those who remain undiagnos...
Highlights
Coronary heart disease (CHD) remains a major public health challenge, and is responsible for approximately one-third of deaths in middle-aged and older adults in the United States.[1]
The SOMAScanÒ proteomic assay was used to identify protein biomarkers associated with risk of cardiovascular events in patients with stable CHD.[3,4]
We focus on a targeted model for predicting myocardial infarction (MI) among patients with stable CHD
Summary
Coronary heart disease (CHD) remains a major public health challenge, and is responsible for approximately one-third of deaths in middle-aged and older adults in the United States.[1]. The phenotypic and genotypic complexity of the disease makes it an ideal target for biomarker discovery, increasingly augmented by high-throughput omics technology.[2] In previous study, the SOMAScanÒ proteomic assay was used to identify protein biomarkers associated with risk of cardiovascular events in patients with stable CHD.[3,4] The resulting model provides improved ability over existing clinical-risk tools and has broad applicability and generalizability among a composite endpoint of cardiovascular events. Diagnosis of a disease with multiple possible outcomes combined with high-dimensional data such as proteomics is both a challenging problem and rich opportunity in translational medicine. We focus on a targeted model for predicting MI among patients with stable CHD. This article belongs to a special issue on Machine Learning
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