Data science is the branch of science that deals with analyzing data using a series of advanced scientific techniques and tools to extract valuable information that can be used to make smart business decisions and strategic planning. Data science is revolutionizing every industry, especially the healthcare industry, which accounts for almost 30% of the world’s data that is being generated. Medical research shows that the human body produces roughly 2 terabytes of data per day, which includes information on blood sugar levels, heart rates, brain activity, and many other variables. Thus, data science has been pivotal in managing massive volumes of medical data and facilitating the effective operation of the healthcare sector.
As such, working in the healthcare sector as a data science practitioner will allow you to greatly serve society. Enrolling in a Data Science bootcamp Online, can help you expedite your journey into the data science realm by acquiring the necessary skills and advanced knowledge.
In this article, we will take a look into the significant role data science has played in the advancement of the healthcare industry.
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1. Medical Imaging
Data science has played a major role in elevating the healthcare industry. One of the major areas where data science is being applied is in the analysis of medical images. Apparently, medical images constitute 90% of all medical data. The medical imaging techniques include X-ray, sonography, MRI (Magnetic Resonance Imaging), mammography, CT scan, and others. These techniques are used by doctors to identify any abnormalities in the inner human anatomy by analyzing the images from these tests. With the help of deep-learning-based algorithms in data science, the diagnostic accuracy and efficiency of medical imaging tests are greatly enhanced, due to which doctors are now able to provide better diagnoses and make better treatment plans.
2. Genomics
Genomics is an important branch of genetics where one studies the entire genome of a living being consisting of genes, which ultimately are made up of DNA. Genomics has proved to play a vital role in the advancement of medical science by helping researchers to identify irregularities or anomalies in the DNA sequence, thus facilitating research on genes. Thus, it has paved the way for better disease diagnosis, identifying a correlation between genes and their effect on a person’s health condition. The application of data science in genomics has helped researchers to extract information from gene sequences and study DNA. This has led to the development of gene therapy, targeted drug delivery, and shedding more light on genetic and hereditary diseases like Alzheimer’s, Parkinson’s disease, etc.
Data science tools like MapReduce, SQL, Galaxy, and Bioconductor are used for data analysis and retrieving information from genetic data, analyzing it, and retrieving information from genomic databases.
3. Facilitates Drug Development
Immunization and medications go a long way in preventing diseases. Drug development is an extremely time-consuming and redundant process. During the testing and formulation stages of drug development, a great deal of data is produced, which needs to be carefully examined and analyzed. With the help of data science, scientists can quickly review millions of test results and accelerate the release of a medication or vaccine after determining its efficacy. Researchers can predict the pattern of drug reaction to the causative agent and the effects of drugs on the human body. Data science has enabled the application of Artificial Intelligence, Machine learning tools, and algorithms in cross-evaluating data from millions of case studies.
4. Predictive Analysis for Accurate Diagnosis
Data science uses predictive analysis algorithms to help identify patterns and relationships in data. This is tremendously useful in healthcare, particularly in disease diagnosis and intervening early to prevent negative effects especially in the treatment of terminal diseases. Predictive analysis, for example, can be used to assess the stages of cancer, the rate at which it spreads, and the level of damage. Predictive analysis can also be used in Chronic disease management, predicting a patient’s health condition and providing prevention strategies by analyzing data collected from a patient’s medical records.
5. Wearables
The latest advancements in technology like the Internet of Things (IoT) has enabled amazing connectivity. The power of IoT can be harnessed in the field of data science for continuous health monitoring of patients. For instance, doctors can receive notifications on their cellphones regarding patients’ health problems thanks to wearable devices like fitness monitors and smartwatches that employ real-time analytics. The wearables’ sensors are able to detect even the smallest variations in blood pressure, temperature, stress level, heart rate, and other medical data. This helps in forecasting potential health risks and devising strategies to prevent further health deterioration.
6. Virtual support
Data science in healthcare has immense potential in the development of software with virtual support. This helps in the development of platforms that provide custom-made experiences for each patient. Individualized experiences can be made in a sophisticated and simpler manner where patients need to only input their symptoms, and the software will predict the disease and its prognosis. It can thus be used to provide valuable suggestions regarding the medication and treatment plan the patient needs to undergo. It can also be very helpful in alerting patients to the importance of taking their medications on time and in averting situations involving medical negligence.
7. Predicting Strokes in Patients
Data science has revolutionized the application of predictive analysis techniques in the medical science domain to identify patients at a higher risk of PFO-related complications like a stroke. PFO, or Patent Foramen Ovale, is a tiny opening that exists between the heart’s upper left and right chambers, or atria. Blood could pass through this hole and into the atria. However, they are associated with an increased risk of ischemic stroke in individuals. Data science professionals can evaluate patient medical records and imaging data to find patterns using machine learning algorithms and predictive analysis models in providing accurate risk assessments and assisting in prompt treatment procedures.
Conclusion
Thus, in conclusion, it can be said that data science has a revolutionary and transformative future in the healthcare sector. The integration of data-driven approaches can greatly enhance the medical science domain with better healthcare options, personalized treatment optimization, and, ultimately, the efficient management of the healthcare system. Check out Data Science Courses here.
