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» Authors » John M Pfeifer

John M Pfeifer

Explore the profile of John M Pfeifer including associated specialties, affiliations and a list of published articles. Areas
Snapshot
Articles 8
Citations 240
Followers 0
Related Specialties
Cardiology & Vascular Diseases
Radiology
General Medicine
Top 10 Co-Authors
Brandon K Fornwalt
Joseph B Leader
Linyuan Jing
Sushravya Raghunath
Christopher M Haggerty
David P vanMaanen
Dustin N Hartzel
Christopher W Good
H Lester Kirchner
Noah Zimmerman
Published In
Circulation
JACC Cardiovasc Imaging
Nat Biomed Eng
J Electrocardiol
J Womens Health (Larchmt)
Affiliations
Soon will be listed here.
Recent Articles
1.
An ECG-based machine learning model for predicting new-onset atrial fibrillation is superior to age and clinical features in identifying patients at high stroke risk
Raghunath S, Pfeifer J, Kelsey C, Nemani A, Ruhl J, Hartzel D, et al.
J Electrocardiol . 2022 Nov; 76:61-65. PMID: 36436476
Background: Several large trials have employed age or clinical features to select patients for atrial fibrillation (AF) screening to reduce strokes. We hypothesized that a machine learning (ML) model trained...
2.
rECHOmmend: An ECG-Based Machine Learning Approach for Identifying Patients at Increased Risk of Undiagnosed Structural Heart Disease Detectable by Echocardiography
Ulloa-Cerna A, Jing L, Pfeifer J, Raghunath S, Ruhl J, Rocha D, et al.
Circulation . 2022 May; 146(1):36-47. PMID: 35533093
Background: Timely diagnosis of structural heart disease improves patient outcomes, yet many remain underdiagnosed. While population screening with echocardiography is impractical, ECG-based prediction models can help target high-risk patients. We...
3.
Promise and Frustration: Machine Learning in Cardiology
Fornwalt B, Pfeifer J
Circ Cardiovasc Imaging . 2021 Jun; 14(6):e012838. PMID: 34126753
No abstract available.
4.
Deep Neural Networks Can Predict New-Onset Atrial Fibrillation From the 12-Lead ECG and Help Identify Those at Risk of Atrial Fibrillation-Related Stroke
Raghunath S, Pfeifer J, Ulloa-Cerna A, Nemani A, Carbonati T, Jing L, et al.
Circulation . 2021 Feb; 143(13):1287-1298. PMID: 33588584
Background: Atrial fibrillation (AF) is associated with substantial morbidity, especially when it goes undetected. If new-onset AF could be predicted, targeted screening could be used to find it early. We...
5.
Deep-learning-assisted analysis of echocardiographic videos improves predictions of all-cause mortality
Cerna A, Jing L, Good C, vanMaanen D, Raghunath S, Suever J, et al.
Nat Biomed Eng . 2021 Feb; 5(6):546-554. PMID: 33558735
Machine learning promises to assist physicians with predictions of mortality and of other future clinical events by learning complex patterns from historical data, such as longitudinal electronic health records. Here...
6.
Prediction of mortality from 12-lead electrocardiogram voltage data using a deep neural network
Raghunath S, Cerna A, Jing L, vanMaanen D, Stough J, Hartzel D, et al.
Nat Med . 2020 May; 26(6):886-891. PMID: 32393799
The electrocardiogram (ECG) is a widely used medical test, consisting of voltage versus time traces collected from surface recordings over the heart. Here we hypothesized that a deep neural network...
7.
Quantifying Sex Differences in Cardiovascular Care Among Patients Evaluated for Suspected Ischemic Heart Disease
Ladapo J, Pfeifer J, Pitcavage J, Williams B, Choy-Shan A
J Womens Health (Larchmt) . 2018 Dec; 28(5):698-704. PMID: 30543478
Cardiovascular care sex differences are controversial. We examined sex differences in management and clinical outcomes among patients undergoing noninvasive testing for ischemic heart disease (IHD). In a rural integrated healthcare...
8.
Association of Patient Beliefs and Preferences With Subsequent Testing After Initial Evaluation for Ischemic Heart Disease
Ladapo J, Pfeifer J, Choy-Shan A, Pitcavage J, Williams B
JACC Cardiovasc Imaging . 2016 Dec; 10(9):1076-1078. PMID: 28017386
No abstract available.