Role of Big Data Analytics in Cardiology
The evidence-based methodologies in the field of cardiology are now an age-old approach. In this time and space, the importance of technological advancements has taken a front seat. Whatever changes we’re now witnessing in the cardiology domain result from a modified approach using technologies like Big Data, AI, etc., which has given it a new dimension. And yes, the role of big data analytics in cardiology is so much more to be discovered.
Big Data has transformed massive amounts of data sets that make no apparent sense into a set of data with a clear analytical purpose. The power of Big Data and AI allowed better data processing and analysis and enabled better monitoring and evaluation of that data for various medical and clinical applications.
Now the question is where has big data analytics played a role in the cardiology domain. Cardiovascular disease research fields are enhanced mostly with the help of big data. Here are some of them:
- Smartphone and Smartwatch Applications: Identification and recording of cardiac arrhythmia are now possible with these applications.
- Cardiac MRI: An imaging technique known as magnetic resonance imaging.
- Variable data integration: Cardiac MRI, genetic data, and biomarker data can be integrated through big data applications.
- Drug surveillance: Drug usage events are analyzed after the drug launch in the market.
- Cause detection and case analysis of heat failure: Made possible through various machine learning algorithms.
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Big Data Analytics and High-Performance Computing(HPC)
Genomic Study, which is basically studying an organism’s complete set of DNA, is believed to have an extended research scope through the application of big data and high-performance computing. All thanks to the union of the two technologies resulting in better decision making.
Alirocumab (Praluent); the first monoclonal antibody that inactivates the PCSK9, a circulating negative regulator of the LDL receptors in the liver. By blocking PCSK9’s ability to work, more receptors are available to get rid of LDL cholesterol from the blood thereby preventing cardiovascular disease. Such drugs that target a specific gene that has been proved to be the cause of cardiac disease are known as ‘inductive drugs.’
Furthermore, identifying the patients who carry such cardiovascular disease-causing genes, tracing the progression of the disease and the effect of the drug acting on that gene is all achievable with the help of big data analytics. All this analysis can be done via the populated data available in the genomic databases and electronic health records(EHRs).
There are quite a few viable resources available for data storage, data analysis, and parallel computing like Hadoop and Google’s Map Reduce. Moreover, data analytics can act as a monitoring tool where alerts could be configured for radiation doses, patient throughput, and procedural times in the catheterization laboratory.
In cardiology, the strengthening of the patient through cell phones and applications will be a field of outstanding turn of events, basically for outpatient management during a cardiovascular breakdown, and decreasing exorbitant readmissions, yet in addition in atrial fibrillation and various therapeutic perspectives.