240 citations · 246 across the 5 of their papers we have counts for
5 papers
Electrocardiographic Deep Learning for Predicting Post-Procedural Mortality
David Ouyang, John Theurer, Nathan R. Stein +21
Background. Pre-operative risk assessments used in clinical practice are limited in their ability to identify risk for post-operative mortality. We hypothesize that electrocardiogr…
High-Throughput Precision Phenotyping of Left Ventricular Hypertrophy with Cardiovascular Deep Learning
Grant Duffy, Paul P Cheng, Neal Yuan +15
Left ventricular hypertrophy (LVH) results from chronic remodeling caused by a broad range of systemic and cardiovascular disease including hypertension, aortic stenosis, hypertrop…
A Single Visualization Technique for Displaying Multiple Metabolite-Phenotype Associations
Mir Henglin, Teemu Niiranen, Jeramie D. Watrous +10
More advanced visualization tools are needed to assist with the analyses and interpretation of human metabolomics data, which are rapidly increasing in quantity and complexity. Usi…
Quantitative Comparison of Statistical Methods for Analyzing Human Metabolomics Data
Brian L. Claggett, Joseph Antonelli, Mir Henglin +10
Background. Emerging technologies now allow for mass spectrometry based profiling of up to thousands of small molecule metabolites (metabolomics) in an increasing number of biosamp…
Statistical Methods and Workflow for Analyzing Human Metabolomics Data
Joseph Antonelli, Brian Claggett, Mir Henglin +9
High-throughput metabolomics investigations, when conducted in large human cohorts, represent a potentially powerful tool for elucidating the biochemical diversity and mechanisms u…