Write in a clean editor, then format for Cardiovascular Digital Health Journal in one click — DocuGuru applies the official Elsevier template with numbered references and exports a submission-ready PDF plus the editable LaTeX source. Free to start.
Cardiovascular Digital Health Journal is a peer-reviewed journal published by Elsevier, covering ECG Monitoring and Analysis, Atrial Fibrillation Management and Outcomes, Cardiac pacing and defibrillation studies.
| Publisher | Elsevier |
|---|---|
| Reference style | Numbered (Elsevier) Numbered — [1], [2] in the text [1] A. Smith, B. Jones, C. Lee, A representative article title, Cardiovascular Digital Health Journal 12 (2023) 45–58.
Formats any DOI in Cardiovascular Digital Health Journal style. No sign-up. |
| Publishes research in | ECG Monitoring and Analysis Atrial Fibrillation Management and Outcomes Cardiac pacing and defibrillation studies Telemedicine and Telehealth Implementation Cardiac electrophysiology and arrhythmias |
| ISSN | 2666-6936 |
| Citation impact (2-yr) | 6.47 |
| h-index | 25 |
| i10-index | 74 |
| Total citations | 2,235 |
| Article processing charge | $2,800 |
| Open access | Yes |
| Top institutions publishing here | University of Massachusetts Chan Medical School |
| Journal website | www.journals.elsevier.com |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
Background: Wearable technologies are increasingly popular. Yet their use remains low by older adults, who may stand the greatest benefit of use. While there is an abundance of research examining the performance, accuracy, specificity, and sensitivity of wearable devices, many barriers remain and need to be addressed to optimize uptake in clinical practice. There is…
BackgroundAutomated computerized electrocardiogram (ECG) interpretation algorithms are designed to enhance physician ECG interpretation, minimize medical error, and expedite clinical workflow. However, the performance of current computer algorithms is notoriously inconsistent. We aimed to develop and validate an artificial intelligence–enabled ECG (AI-ECG) algorithm capable of comprehensive 12-lead ECG interpretation with accuracy comparable to practicing cardiologists.MethodsWe developed…
Cuff-based home blood pressure (BP) devices, which have been the standard for BP monitoring for decades, are limited by physical discomfort, convenience, and their ability to capture BP variability and patterns between intermittent readings. In recent years, cuffless BP devices, which do not require cuff inflation around a limb, have entered the market, offering the…
Landmark studies have demonstrated that wrist-worn wearable devices (eg, smartwatches, fitness bands) can provide potentially actionable data to improve real-time surveillance of respiratory diseases, such as influenza,1Radin J.M. Wineinger N.E. Topol E.J. Steinhubl S.R. Harnessing wearable device data to improve state-level real-time surveillance of influenza-like illness in the USA: a population-based study.Lancet Digital Health. 2020;…
BackgroundSystematic case identification is critical to improving population health, but widely used diagnosis code–based approaches for conditions like valvular heart disease are inaccurate and lack specificity.ObjectiveTo develop and validate natural language processing (NLP) algorithms to identify aortic stenosis (AS) cases and associated parameters from semi-structured echocardiogram reports and compare their accuracy to administrative diagnosis codes.MethodsUsing…
