What is Data Science?
Data science is widely regarded as one of the most essential parts of any industry in today's marketplace, given the massive amounts of data that are produced.
Data Science is growing enormously to occupy all the industries of the world in the current world. In this article, we will understand how data science is transforming the healthcare sector. We will understand various underlying concepts of data science, used in medicine and bio-technology.
Medicine and healthcare are two of the most important parts of our human lives. Traditionally, medicine solely relied on the discretion advised by the doctors. For example, a doctor would have to suggest suitable treatments based on a patient’s underlying symptoms.
However, this wasn’t always correct and was prone to human errors. However, with the advancements in computers and in particular, Data Science, it is now possible to obtain accurate diagnostic measures.
Data Science helps in advancing healthcare facilities and processes. It helps boost productivity in diagnosis and treatment and enhance the workflow of healthcare systems. The ultimate goals of the healthcare system are as follows:
There are several fields in healthcare such as medical imaging, drug discovery, genetics, predictive diagnosis and several others that make use of data science.
Since, Drug Discovery is considered as a highly complicated discipline, the various pharmaceutical industries are heavily relying on data science to solve their problems and create better drugs for the people. Drug Discovery involves heavy financial expenditure and heavy testing. Thus, it is a time-consuming process. Data Science along with Machine Learning Algorithms are revolutionizing this process and providing extensive insights into optimizing the success rate of predictions.
The various pharmaceutical companies use the insights from the patient information such as mutation profiles and also the patient metadata. This information really helps the researchers to develop models and find statistical relationships between the multiple attributes.
Therefore, the companies can design drugs that address the key mutations in the genetic sequences. Deep learning algorithms can find the probability of the development of disease in the human system. The data science algorithms can also help to simulate how the drugs will act in the human body that takes away the long laboratory experimentations.
Data Science plays a major part in monitoring a patient's overall health and notifying necessary steps to be taken in order to prevent potential diseases from occurring. Data Scientists are using powerful predictive analytical tools to detect chronic diseases at an early level.
In most of the extreme cases, there are instances where diseases are not caught at an early stage due to negligibility. This proves to be highly detrimental to not only the patient’s health but also the economic costs. As the disease grows, the cost of curing it also increases. Therefore, data science plays a huge role in optimizing the economic spending on healthcare.
With the help of disease predictive modeling, data scientists have developed a comprehensive virtual platform that provides assistance to the patients. With the help of these platforms, a patient can enter his or her symptoms in the input and get insights about the various possible diseases based on the confidence rate. Furthermore, patients who suffer from psychological problems like depression, anxiety and neurodegenerative diseases like Alzheimer’s can make use of virtual applications to help them in their daily tasks.
Data Science has a key role in IoT i.e., Internet of Things. The various IoT devices that are present as wearable devices that track heartbeat, temperature and other medical parameters of the users. The data that is collected is analyzed with the help of data science. With the help of analytical tools, doctors are able to keep track of a patient's circadian cycle, their blood pressure and also their calorie intake.
Other than wearable monitoring sensors, doctors can monitor a patient’s health through home devices. For patients that are chronically ill, there are several systems that track patient’s movements, monitor their physical parameters and analyze the patterns that are present in the data.
The primary and foremost use of data science in the health industry is through medical imaging. There are various imaging techniques like X-Ray, MRI and CT Scan. All these techniques visualize the inner parts of the human body. Traditionally, doctors would manually inspect these images and find irregularities within them. Previously, it was very difficult to find microscopic deformities and as a result, doctors could not suggest any proper diagnosis.
With the development of deep learning technologies in data science, it is not difficult to find such microscopic deformities in the scanned images. Through image segmentation, it is possible to search for defects present in the scanned images.
Genomics is the study of sequencing and analysis of genomes. A genome consists 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 has inculcated itself in the realms of data science and bigdata.
Before the availability of such powerful computation, the organizations spent a lot of time and money on analyzing the sequence of genes. which was very expensive and a tedious process.
However, with the advanced data science tools, it is now possible to analyze and derive insights from the human gene in a much shorter period of time and at a much lower cost. There are several data science tools like MapReduce, SQL, Galaxy, Bioconductor etc. MapReduce processes the genetic data and reduces the time it takes to process genetic sequences.
Healthcare is widely considered as an important domain for predictive analytics. It is actually one of the most popular topics in health analytics. A predictive model uses historical data, learns from it, finds patterns and generates accurate predictions from it. It finds various correlations and association of symptoms, finds habits, diseases and then makes meaningful predictions.
Predictive Analytics is playing an important role in improving patient care, chronic disease management and increasing the efficiency of supply chains and pharmaceutical logistics.
With data science, hospitals can predict the deterioration in patient’s health and provide preventive measures and start an early treatment that will assist in reducing the risk of the further aggravation of patient health. Furthermore, predictive analytics plays an important role in monitoring the logistic supply of hospitals and pharmaceutical departments.
So, we can conclude that data science has many applications in healthcare and the medicine and healthcare industry has heavily utilized Data Science efficiently for the improving lifestyle of patients and predicting diseases at an early stage.
Furthermore, with advancements in medical image analysis, it is possible for the doctors to find microscopic tumors that were previously hard to find. Therefore, data science has revolutionized healthcare and has helped the medical industry in a lot of ways.
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