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Data Science Applications in Healthcare

Data science is the process of applying advanced scientific techniques and tools in cleansing, collecting, and organizing data to extract valuable information that can be used to make smart business decisions and strategic planning. It is a no-brainer to realize that data science has taken the world by storm and is revolutionizing every industry with its manifold applications. One such sector is the healthcare industry which accounts for almost 30% of the world’s data that is being generated. In the current pandemic era that the world has been experiencing over the last few years, the proverb “Health is Wealth” holds true. With the increase in epidemics and the search for new medicines and vaccines, data science has played a significant role in facilitating advanced medical research and assisting medical professionals in their fight against diseases. Thus, data science is revolutionizing the world’s rapid technological advancement, not just in the healthcare sector but globally. As a result, choosing data science as a career path is an excellent choice for those who want to be a part of the digital revolution.

There is a myriad of data science programs available in both traditional and online settings for those seeking to enhance their skills and increase their knowledge base. For instance, Purdue university, in partnership with IBM, offers one of the best Data science programs. Available online, which was adjudged the Best Data Science Bootcamp of 2021.

There are numerous ways data science can improve the healthcare industry. Below are a few significant applications :

1.     Data Science for Medical Imaging

Medical imaging is one of the most significant areas where data science is being used in the healthcare industry. According to IBM, medical images contain approximately 90% of all medical data. Medical imaging techniques such as X-ray, sonography, MRI (Magnetic Resonance Imaging), mammography used by mammography technicians, CT scan, and others are used by doctors to obtain a clear vision of the inner parts of the human body and detect anomalies in the functioning of internal organs. Previously, doctors would manually scour through this imaging to identify any abnormalities. Any medical errors in diagnosis, however, carried the weight of life due to a lack of image precision. However, in recent years, diagnostic accuracy and efficiency have significantly increased thanks to deep-learning-based algorithms in data science. By feeding algorithms with datasets from older clinical reports and laboratory studies, such deep-learning platforms enable the improvement of accuracy by a factor of many by analyzing and displaying better results.

2.     Data Science for Genomics

All living things are genetically coded, and genomics is the study of an organism’s entire gene pool (genome) using DNA sequencing and bioinformatics techniques. Data science plays a crucial role in the use of statistics and strong computational tools to extract information from the genes’ DNA sequence. With the help of cutting-edge data science tools and Big Data, the Human Genome Project’s successful mapping and sequencing of the entire human genome has sparked a surge in high-level research and tailor-made treatment plans for patients based on their genetic makeup.

Data science is assisting researchers in analyzing genetic strands and detecting anomalies in gene sequences, gaining insight into the relationship between genetics and a person’s health. This has also paved the way for the development of targeted drug delivery by analyzing the drug’s response to a specific genetic issue. Several data science tools, such as MapReduce, SQL, and Bioconductor, are used to process genetic data, analyze it, and retrieve information from genomic databases. All these tools enable efficient and faster data retrieval, reducing the time required to process genomic sequences.

3.     Drug research

The importance of medicines and vaccines in disease resistance was one lesson we all learned from the covid pandemic. As the world’s population grows, so does the number of diseases, making it difficult for the healthcare industry to find drugs and vaccines to combat them. However, years of hard work, research, testing, and trial runs go into developing a medicine before it is released to the public. The drug research process also includes clinical trials that fail to address the parameters, resulting in delays and increased financial costs. Moreover, the testing and formulating generate massive amounts of data that must be methodically analyzed and studied. However, thanks to innovative data science tools, researchers can now process data from millions of tests in a short period of time and expedite the launch of a vaccine or drug after analyzing its efficacy. With the help of automated Artificial Intelligence and machine learning tools and data science algorithms, and cross-evaluating data from millions of case studies, scientists can now predict how the drugs will react to the causative agent and the effects of drugs on the human body, reducing the need for time-consuming laboratory studies.

4.     Predictive Analytics in Healthcare

In today’s world, data translates to information, and the most important aspect of medical diagnosis is information about the patient’s medical history. As a result, efficient systems for collecting a patient’s medical data must be in place so that proper treatment strategies and effective diagnoses can be developed. Medical data can include anything from a patient’s blood group, blood pressure, sugar level, and even hereditary diseases. Using predictive models, the data can be evaluated for patterns and correlations, which is extremely valuable in the treatment of terminal diseases such as cancer, where it can be used to determine the stages of cancer, the rate at which it spreads, and the extent of the damage.

The following are the primary benefits of predictive analytics in healthcare:

  • Chronic disease management
  • Monitors and analyzes pharmaceutical logistics demand.
  • Forecasts a patient’s condition and recommends prevention strategies.
  • Making the most of doctors and other resources to help as many patients as possible.
  • Predicts a patient’s potential future medical emergencies.

5.     Monitoring Patient Health

With the help of wearable devices belonging to the Internet of Things (IoT). realm that uses real-time analytics, doctors are intimated about the health conditions of patients through notifications on their smartphones. The sensors in these wearable devices can pick up minute changes in the heartbeat, blood pressure, temperature, stress level, and other medical parameters. Doctors can then prescribe treatment methods based on this and prevent possible deterioration of a patient’s health condition.

The following are just a few of the many advantages of using data science to remotely monitor a patient’s health:

  • Improved monitoring of patient’s health
  • Facilitating better treatment plans and procedures
  • Reduced wait times for patients seeking medical attention
  • providing remote populations with access to healthcare
  • removing logistical obstacles, especially for patients who are old or disabled
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