Artificial Intelligence based machine learning model has brought another glory to cardiovascular research. When you think of cardiac arrest or any cardiovascular severity, the image that probably comes to mind is of a dark or critical condition, agitation, and simultaneously prayers begin for the survival of your loved ones. Using first-of-his-kind observations on cardiac medical emergencies, Ketan Gupta, a prominent research scientist, has recently discovered an extremely powerful artificial intelligence-based machine learning model that successfully predicts the onset of cardiac arrest emergence and its likelihood of occurrence in the near future.
Today, cardiac arrest is one of the leading causes of mortality in neonates, youth, adults, and the senior population, which is an area of concern and indicates an urgent demand to leverage advanced technology with medical science to improve the survival rate of the patients, says Ketan Gupta. He envisioned the achievement as a machine learning innovation in the frontier of artificial intelligence in medical research.
In an interview, Ketan stated, "The original contribution in this research relies on developing simulation models using statistical analysis, deep learning algorithms, sensitive computational models, and Fourier Transform Infrared Spectroscopy techniques to discover unknown and velocity patterns from partial or missing data and recognize objects accurately from multiple angles." This novel healthcare framework is a complex, interconnected, multi-layered neural network-based model that identifies the severity of cardiac arrest based on the patient's age, ejection fraction of heart chambers, and the patient's follow-up time to the physician.
Despite having many technologies, the accuracy of the investigating techniques and prognosis of cardiac issues is still a big challenge. Ketan's research has already made significant strides in the technology domain. For example, he utilized advanced neural networks and devised a multivariate risk model, including a Feed-Forward Backpropagation neural network which was the first evidence of predicting cardiac arrest at the early stages and its occurrence in the near future. The machine learning model critically examines image pixels and segments them into respective classes for pattern recognition. This AI-induced model incorporates natural language processing (NLP) techniques that assist physicians in feeding raw or real-time unstructured data by voice command in the algorithm. Once the data is processed and structured, the model suggests the best possible solutions for physicians to take immediate actions for cardiac arrest, Ketan says.
Today, cardiovascular diseases are prevalent in newborn babies, which has raised mortality concerns. In earlier studies, several attempts have been made to determine the accuracy of the investigating techniques, but the prognosis of cardiac arrest is still a big challenge. Ketan's research model incorporates a sensitive deep learning algorithm that continuously monitors newborns with heart block defects and calculates its severity based on symptomatic data like fatigue rate, cyanosis (blue defects), slow weight gain, shortness of breath, and other pathological symptoms. His research discovered several critical parameters pertaining to cardiac risks in neonates that were commendable. One of the leading multi-specialty hospitals - Tejankar Healthcare and Medical Research Institute, recognized this research and collaborated with Ketan for further innovation in technology with a common goal of saving the lives of newborn babies.
Many researchers, healthcare, and industry experts believe that very few scientists possess experience with dynamic digital imaging using multivariate classification modeling to identify missing patterns and ejection fractions. They say this discovery is promising and has high potential to improve the survival rate of cardiac patients with high accuracy and dramatically lower treatment costs.
Ketan is an award-winning research scholar with strong technological and healthcare background that showcases his pursuit of excellence. He has 40 publications in eminent journals and conferences that assisted many scientists and clinicians in using his study and implementing them in their ongoing research to obtain promising results. As a machine learning expert, Ketan is invited by leading journals like IJECES, BMC Medical Informatics, JCC, Cybernetics & Systems, and IEEE conferences to review scientific articles and currently he is an active reviewer for 20+ journals and conferences.
Additionally, Ketan is not only an accomplished scientist but also a seasoned mentor in the field of artificial intelligence and machine learning. He is also well-positioned as a Sr. IT Program Manager in R&D at Meta (Facebook), one of the prestigious organizations. With these incredible achievements and experience, Ketan was invited to the panel to be a judge and assess global technological nominations at Globee Business Awards.
Technological advancements are not just about fancy lights or new phone games. They require immense effort, discipline, dedication, and determination to accept the challenges and develop innovative gateways to overcome them. Ketan is a passionate research scientist who always strives for excellence and is involved in implementing technology in the healthcare domain for the betterment of community welfare and social well-being.
