Christopher Reeder
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View article: Electrocardiogram-Based Artificial Intelligence to Identify Coronary Artery Disease
Electrocardiogram-Based Artificial Intelligence to Identify Coronary Artery Disease Open
Artificial intelligence-enabled analysis of the ECG may facilitate identification of individuals with possible undiagnosed CAD and inform downstream testing and preventive measures.
View article: Mapping GLAMs: creating a national dataset of GLAMs to develop a categorical climate-change risk assessment scale
Mapping GLAMs: creating a national dataset of GLAMs to develop a categorical climate-change risk assessment scale Open
Introduction. The PROTECCT-GLAM project aims to assess and address climate risks for U.S. galleries, libraries, archives, and museums (GLAMs). The project team began with creating a national dataset of GLAMs. Method. The project team used …
View article: Contemporary Burden of Cardiovascular Disease in Pregnancy: Insights from a Real-World Pregnancy Electronic Health Record Cohort
Contemporary Burden of Cardiovascular Disease in Pregnancy: Insights from a Real-World Pregnancy Electronic Health Record Cohort Open
Importance Cardiovascular disease (CVD) is the leading cause of maternal morbidity and mortality, however the contemporary burden and secular trends in pregnancy-related CV complications are not well characterized. Objective We sought to e…
View article: Unsupervised deep learning of electrocardiograms enables scalable human disease profiling
Unsupervised deep learning of electrocardiograms enables scalable human disease profiling Open
The 12-lead electrocardiogram (ECG) is inexpensive and widely available. Whether conditions across the human disease landscape can be detected using the ECG is unclear. We developed a deep learning denoising autoencoder and systematically …
View article: Deep learning-derived splenic radiomics, genomics, and coronary artery disease
Deep learning-derived splenic radiomics, genomics, and coronary artery disease Open
Background Despite advances in managing traditional risk factors, coronary artery disease (CAD) remains the leading cause of mortality. Circulating hematopoietic cells influence risk for CAD, but the role of a key regulating organ, spleen,…
View article: Frequency of Electrocardiogram-Defined Cardiac Conduction Disorders in a Multi-Institutional Primary Care Cohort
Frequency of Electrocardiogram-Defined Cardiac Conduction Disorders in a Multi-Institutional Primary Care Cohort Open
Cardiac conduction disorders are common in a primary care population, especially among older individuals with cardiovascular risk factors.
View article: Natural Language Processing for Adjudication of Heart Failure in a Multicenter Clinical Trial
Natural Language Processing for Adjudication of Heart Failure in a Multicenter Clinical Trial Open
Importance The gold standard for outcome adjudication in clinical trials is medical record review by a physician clinical events committee (CEC), which requires substantial time and expertise. Automated adjudication of medical records by n…
View article: Deep learned representations of the resting 12-lead electrocardiogram to predict at peak exercise
Deep learned representations of the resting 12-lead electrocardiogram to predict at peak exercise Open
Aims To leverage deep learning on the resting 12-lead electrocardiogram (ECG) to estimate peak oxygen consumption (V˙O2peak) without cardiopulmonary exercise testing (CPET). Methods and results V ˙ O 2 peak estimation models were developed…
View article: Natural Language Processing for Adjudication of Heart Failure Hospitalizations in a Multi-Center Clinical Trial
Natural Language Processing for Adjudication of Heart Failure Hospitalizations in a Multi-Center Clinical Trial Open
Background The gold standard for outcome adjudication in clinical trials is chart review by a physician clinical events committee (CEC), which requires substantial time and expertise. Automated adjudication by natural language processing (…
View article: Genetic Susceptibility to Atrial Fibrillation Identified via Deep Learning of 12-Lead Electrocardiograms
Genetic Susceptibility to Atrial Fibrillation Identified via Deep Learning of 12-Lead Electrocardiograms Open
BACKGROUND: Artificial intelligence (AI) models applied to 12-lead ECG waveforms can predict atrial fibrillation (AF), a heritable and morbid arrhythmia. However, the factors forming the basis of risk predictions from AI models are usually…
View article: Deep Learning of Electrocardiograms Enables Scalable Human Disease Profiling
Deep Learning of Electrocardiograms Enables Scalable Human Disease Profiling Open
The electrocardiogram (ECG) is an inexpensive and widely available diagnostic tool, and therefore has great potential to facilitate disease detection in large-scale populations. Both cardiac and noncardiac diseases may alter the appearance…
View article: One Clinician Is All You Need–Cardiac Magnetic Resonance Imaging Measurement Extraction: Deep Learning Algorithm Development
One Clinician Is All You Need–Cardiac Magnetic Resonance Imaging Measurement Extraction: Deep Learning Algorithm Development Open
Background Cardiac magnetic resonance imaging (CMR) is a powerful diagnostic modality that provides detailed quantitative assessment of cardiac anatomy and function. Automated extraction of CMR measurements from clinical reports that are t…
View article: Genetic Susceptibility to Atrial Fibrillation Identified via Deep Learning of 12-lead Electrocardiograms
Genetic Susceptibility to Atrial Fibrillation Identified via Deep Learning of 12-lead Electrocardiograms Open
Artificial intelligence (AI) models applied to 12-lead electrocardiogram (ECG) waveforms can predict atrial fibrillation (AF), a heritable and morbid arrhythmia. We hypothesized that there may be a genetic basis for ECG-AI based risk estim…
View article: ECG-Based Deep Learning and Clinical Risk Factors to Predict Atrial Fibrillation
ECG-Based Deep Learning and Clinical Risk Factors to Predict Atrial Fibrillation Open
Background: Artificial intelligence (AI)–enabled analysis of 12-lead ECGs may facilitate efficient estimation of incident atrial fibrillation (AF) risk. However, it remains unclear whether AI provides meaningful and generalizable improveme…
View article: Cohort Design and Natural Language Processing to Reduce Bias in Electronic Health Records Research: The Community Care Cohort Project
Cohort Design and Natural Language Processing to Reduce Bias in Electronic Health Records Research: The Community Care Cohort Project Open
Background Electronic health records (EHRs) promise to enable broad-ranging discovery with power exceeding that of conventional research cohort studies. However, research using EHR datasets may be subject to selection bias, which can be co…
View article: Comparison of parasitic mite retrieval methods in a population of community cats
Comparison of parasitic mite retrieval methods in a population of community cats Open
Objectives This study compared methods of mite retrieval from community cats in the Ohio River Valley region of the USA and determined incidence of parasitic mites in this region. Methods In total, 493 community cats were humanely trapped …
View article: High Resolution Mapping of Enhancer-Promoter Interactions
High Resolution Mapping of Enhancer-Promoter Interactions Open
RNA Polymerase II ChIA-PET data has revealed enhancers that are active in a profiled cell type and the genes that the enhancers regulate through chromatin interactions. The most commonly used computational method for analyzing ChIA-PET dat…