How Is Data Science Creating Ripples In The Healthcare Industry
The two most pivotal parts of our human lives are medicine and healthcare. For the longest time, medicine had to rely solely on the discretion exercised by the doctors. However, this wasn't always correct or the most accurate way of going about things. And this doesn't really come as a surprise because this method was highly prone to human errors. However, with the advancements in computers and in particular, Data Science, it is now possible to obtain accurate diagnostic measures.
In the current scheme of things, multiple fields in healthcare like genetics, medical imaging, predictive diagnosis, and drug discovery are making active use of data science and predictive analytics to create headlines with every passing day. Further, huge volumes of health-related information have now been made extremely accessible thanks to the widespread adoption of wearable tech. This has, in turn, opened up new opportunities for better and more informed healthcare, ushering in a new era of sorts. If you've been toying with the idea of taking an online predictive analytics course, you won't find a better time than the present. Curious to know how data science is creating ripples in the world of healthcare? Read on to find out!
Better Monitoring of Health Problems Courtesy of Wearables Data
Our body generates almost two terabytes worth of daya every single day. Thanks to technology, we now have the luxury of collecting and analyzing it to gain breath-takign insights into sleep patterns, stress levels, brain activity, heart rate, and even blood glucose. Equipped with such amount of health data, scientists are pushing the boundaries in health monitoring.
Further, machine learning algorithms can be emnployed to efficiently track and detect cardiovascular and respiratory disorders. The technology that powers modern-day wearables is capable of detecteding the slightest of changes in the patient's health indicators. Subsequently, they are programmed to alert the wearers attending doctor or health supervisor in the event of extremely skewed readings.
The data derived from wearables also empowers the data scientists working in the healthcare industry to come up with new breakthroughs with respect to chronic disease management. The primary objective of this research is to improve the life quality of individuals that have been diagnosed with dangerous chronic conditions, such as diabetes and hypertension.
Data scientists process patient's behavioral data, as observed from their wearables, to develop highly customized programs for every patient. In the process, they provide doctors and personal health coaches with an opportunity to gain deeper insights into the patient's health and adjust the programs along the way. Furthermore, the self-learning algorithm is constantly improved as it sources more patient data from the system.
Massive Progress In The Field of Genomics
Genomics refers to the study of sequencing and analysis of genomes. A genome is made up of the DNA and all the genes of the organisms. Ever since the compilation of the Human Genome Project, the research has been advancing rapidly and the rise of big data and data science has only seen this movement ascend new heights.
Before organizations had access to powerful computational tools, they had to shell out huge amounts of time and money to analyz the sequence of genes. The entire process was tedious, and not to mention, highly expensive. With the advent of data science tools, however, it has now become possible to analyze and derive insights from the human gene in a much shorter period of time and in a much lower cost. The primary objective of the research scientists operating in this domain is to analyze the genomic strands and identify irregularities and defects in them. This enables them to establish connections between genetics and the health of the individual under question.
Typically, researchers use data science for analyzing the genetic sequences, with the aim of identifying a correlation between the parameters contained within it and the disease. Additionally, research in genomics also includes finding the right drug and gaining a deeper insight in the way a drug responds to a particular genetic issue. There is in fact, a recent discipline that combines data science and genetics called Bioinformatics. Althoug plenty od research has already been conducted in the field of computational biology and bioinformatics, there is still a lot waiting to be uncovered. There are advanced fields that are still being researched such as genetic risk prediction, gene expression prediction, and the kind.
Improved Diagnostic Efficiency and Accuracy
There now exist deep learning algorithms that are read imaging data such as CT scans, X-rays, and the like and analyze it, checking the given results against extensive database of clinical reports and laboratory studies. As a result, this improves the diagnostic accuracy and efficiency by leaps and bounds. Data Science and machine learning algorthims when coupled with sound recognition technologies result in diagnostic opportunities that were hitherto unimaginable. At present, a mobile app, powered by data science technologies, presents a significant opportunity for better diagnosis and more efficient disease monitoring.
Delivering Virtual Assistance
With the help of data science and predictive modeling algorithms, researches have been able to come up with comprehensive virtual platforms through which nurses, doctors, and other medical staff can receive real-time updates regarding the condition of their patients. As a result, this gives them the scope of delivering patient care remotely and makes life a lot easier for them. As these platforms provide doctors with an accurate report of the patient's vitals and health condition, the margin of an erroneous diagnosis is minimized drastically. Subsequently, it empowers doctors to prescribe the most accurate medicine to their patients.
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