About this Research Topic
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'.
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'.
Keywords: Disease detection, Cardiovascular Disease Prevention, Electophysiology, Interventional Cardiology, Digital Health and Wearable devices, Electronic Health Records, Drug Development
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
