Cardiovascular diagnoses and therapy are dependent on integrating, analyzing and controlling multimodal data. The advent of computational statistics, machine learning and artificial intelligence has aided in earlier risk factor detection, aggressive disease treatment, improved prognosis and reduced complications. Monitors, Internet of medical things (IoMT) and Electronic Health Record integrations have accelerated diagnosis and treatment. Multi-omic studies utilizing big data have allowed deeper insights of the pathophysiology and treatment of disease to allow precision.

Cardiovascular diagnostics entail complex assimilation and curation of data, followed by interpretation using multi modal skillets. Data beginning from public health databases to individual monitors require large scale assessment to identify patterns and trends to allow us to assist diagnostics comparable to those that would be made by humans. We also would like to learn the scope of secondary assessments that identify complex, widely distributed patterns not visible to the human eye and test it against real-world scenarios.

Precision cardiology focuses on taking multiple parameters into account, and uses scoring systems that were created via traditional or advanced statistical methods based on prior data. Expanding them to involve larger datasets to improve present scoring/assessment systems will allow higher level of precision care. Such research requires interdisciplinary contributions from cardiologists, data scientists, engineers and bioinformaticians to come together to solve these challenges of the future.

This Research Topic calls for the following themes:

1. Precision cardiology studies utilizing EHR integration in patient care

2. Cardiovascular genomics, genome-wide association studies

3. Epidemiological and exposomal cardiovascular research

4. Cardiovascular multi-omics research

5. IoMT device data science, and big data studies

6. Advanced statistical, machine learning and artificial intelligence methods in cardiovascular care and data science

7. Electronic health record integration for precision care in cardiology

8. Image analysis, of echocardiograms, MRI

9. AI - computer aided diagnostics: machine and deep learning approaches

10. Risk prediction based on AI

11. Natural language processing - for patient data gathering, literature review, knowledge discovery

The Topic welcomes manuscripts types as defined 'here'.

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Frontiers in Molecular Medicine

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