Computational and Mathematical Methods in Medicine

Computational Advances in Cardiovascular Health


Publishing date
01 Jan 2019
Status
Published
Submission deadline
17 Aug 2018

1University of Texas Health Sciences Center, Houston, USA

2Harvard University, Boston, USA

3Tulane University School of Medicine, New Orleans, USA


Computational Advances in Cardiovascular Health

Description

Cardiovascular disease is the world’s top mortality cause, accounting for 1 in 3 deaths and 1 in 5 dollars of the American healthcare system. Health inequities, omics, and social determinants of health including diet, exercise, and smoking as behaviors embedded within individuals’ larger social networks are increasingly promising comprehensive targets of population health management and precision medicine. Computational advances driven by machine learning and next generation analytics are showing increasing potential to unite these two movements to ultimately improve clinical, cost, and equity outcomes. Such advances are helping accelerate translational omics including genomics and epigenetics, geographic information system (GIS) heat mapping, social network mapping, prior-to-launch social innovation simulations, and health system redesign based on predictive models of patient demand and market dynamics. Yet there are no widely accepted evidence-based standards in these computational advances, nor how to optimally apply them to help reverse the cardiovascular disease epidemic.

The aim of this special issue is to combine multidisciplinary research on computational advances that have immediate applications to the cardiovascular disease epidemic as either novel applications of those advances to this epidemic or theoretical developments that can be applied to it. This issue especially welcomes research articles that demonstrate such new computational advances (including novel machine learning algorithms, statistical, and/or mapping methodologies) on this topic. Review articles are also encouraged that could summarize the state-of-the-art methods and algorithms and their applications in this field.

Potential topics include but are not limited to the following:

  • Population health management
  • Precision medicine
  • Social innovation
  • Social determinants of health
  • Social networks
  • Cost effectiveness
  • Health system redesign
  • Machine learning
  • Causal inference statistics
  • Geographic information system heat mapping
  • Bayesian adaptive trials and other novel study design advances

Articles

  • Special Issue
  • - Volume 2019
  • - Article ID 2985984
  • - Editorial

Computational Advances in Cardiovascular Health

Dominique J. Monlezun | Francesco Nordio | Tianhua Niu
  • Special Issue
  • - Volume 2019
  • - Article ID 9682138
  • - Research Article

Model-Based Quantification of Left Ventricular Diastolic Function in Critically Ill Patients with Atrial Fibrillation from Routine Data: A Feasibility Study

Nicholas Kiefer | Maximilian J. Oremek | ... | Sven Zenker
  • Special Issue
  • - Volume 2018
  • - Article ID 1380348
  • - Research Article

Arrhythmia Classification of ECG Signals Using Hybrid Features

Syed Muhammad Anwar | Maheen Gul | ... | Majdi Alnowami
  • Special Issue
  • - Volume 2018
  • - Article ID 4517652
  • - Research Article

Biomechanical Aspects of Closing Approaches in Postcarotid Endarterectomy

Idit Avrahami | Dafna Raz | Oranit Bash
  • Special Issue
  • - Volume 2018
  • - Article ID 5284969
  • - Research Article

The Time-Domain Integration Method of Digital Subtraction Angiography Images

Shuo Huang | Le Cheng | ... | Suiren Wan
  • Special Issue
  • - Volume 2018
  • - Article ID 4860204
  • - Research Article

Analysis of Exercise-Induced Periodic Breathing Using an Autoregressive Model and the Hilbert-Huang Transform

Tieh-Cheng Fu | Chaur-Chin Chen | ... | Hsueh-Ting Chu
  • Special Issue
  • - Volume 2018
  • - Article ID 7126532
  • - Research Article

Wall Shear Stress Estimation of Thoracic Aortic Aneurysm Using Computational Fluid Dynamics

J. Febina | Mohamed Yacin Sikkandar | N. M. Sudharsan
Computational and Mathematical Methods in Medicine
 Journal metrics
Acceptance rate38%
Submission to final decision61 days
Acceptance to publication39 days
CiteScore1.840
Impact Factor1.563
 Submit

We are committed to sharing findings related to COVID-19 as quickly and safely as possible. Any author submitting a COVID-19 paper should notify us at help@hindawi.com to ensure their research is fast-tracked and made available on a preprint server as soon as possible. We will be providing unlimited waivers of publication charges for accepted articles related to COVID-19. Sign up here as a reviewer to help fast-track new submissions.