A study found that every human body creates 2 terabytes of data every day. This information includes brain activity, stress level, heart rate, blood sugar level, and a lot more.
We now have more advanced technologies, like Data Science, that help us deal with such a large amount of data. It helps keep track of patients’ health by keeping records.
With the help of Data Science in healthcare, it is now possible to find the signs of a disease at a very early stage. Also, many new tools and technologies have made it possible for doctors to check on their patients from far away.
In the past, doctors and people in charge of hospitals couldn’t deal with a lot of patients at once. And because the patients weren’t getting the right care, their conditions would get worse.
Now, though, things are different. With the help of Data Science and Machine Learning applications, wearable devices can let doctors know about a patient’s health. Then, the hospital can send their junior doctors, assistants, or nurses to the homes of these patients.
Hospitals can also put in different tools and equipment to help doctors figure out what’s wrong with these patients. Built on top of Data Science, these devices can get information from patients like their heart rate, blood pressure, body temperature, and so on.
Doctors get this information about their patient’s health in real-time through updates and alerts in mobile apps. Then, they can figure out what’s wrong and help the junior doctors or nurses treat the patients at home. This is how Data Science helps doctors use technology to take care of patients.
Benefits of Data Science in Healthcare
Data Science helps make healthcare facilities and procedures better. It speeds up the process of diagnosing and treating patients and makes healthcare systems run better. Here are the ultimate goals of the health care system:
- To Improve the Efficiency of the Healthcare System
- Reduce the Likelihood That a Treatment Won’t Be Effective
- Ensuring That People Receive Appropriate Care When They Require It.
- Help Avert Unneeded Emergencies When There Are No Doctors Available.
- Reducing the Amount of Time Patients Must Wait
The Role of a Data Scientist in Healthcare
A Data Scientist’s job is to use all of Data Science’s techniques to build them into healthcare software. The Data Scientist extracts useful insights from the data to make predictive models. Overall, a Data Scientist in healthcare is in charge of the following:
- Collecting information from patients
- Analyzing the requirements of hospitals
- Data organization and classification for usage
- Data analytics performed with a variety of tools
- Applying algorithms to the data to draw conclusions
- Along with the development team, developing predictive models
Let’s now talk about the main uses of data science in the healthcare industry.
Medical Image Analysis
A promising area for exploiting technological improvements in medicine and healthcare. The development of data science has helped the healthcare industry reach new heights. It is useful in several areas, including the interpretation of medical images. One of the most fascinating research topics in image recognition technology is.
Data science aids in the recognition of scanned images to identify the flaws in a human body and assist doctors in developing a successful treatment plan. X-rays, sonograms, MRIs (Magnetic Resonance Imaging), CT scans, and many other medical picture tests are among them.
Doctors can treat patients more effectively by properly analyzing the images from these examinations. The standard imaging methods are those. However, the use of data science has further revolutionized the healthcare sector through these imaging techniques.
Data science employs a variety of techniques to identify variations in picture and resolution states and to examine orthogonality. Data scientists are developing increasingly sophisticated methods to raise the standard of image analysis to effectively extract medical data from images.
A recent study on utilizing Deep Learning to diagnose skin conditions was released by Google AI. The Deep Learning model was developed in such a way that it has a 97 percent accuracy rate for diagnosing 26 skin disorders.
Deep neural networks, machine learning, and data science are used to make the diagnosis. Let’s examine the three popular algorithms for medical picture analysis now:
- Anomaly detection algorithm: This algorithm aids in spotting abnormalities like bone displacements and fractures.
- Image processing algorithm: Image analysis, enhancement, and denoising are all made possible by the image processing method.
- Descriptive image recognition algorithm: The program visualizes and collects information from photos, interprets it, and uses it to create a larger picture (for example, merging the images of the brain scan and designating them accordingly)
Both supervised and unsupervised methods are successfully used to implement these techniques.
Predictive Analytics in Healthcare
Information is one of the key components of healthcare analytics in the modern era. The illness may worsen as a result of a lack of accurate information on the patient. Therefore, accurate information or data on the patient must be gathered.
This information can include everything from the patient’s body temperature, blood pressure, and sugar level. Data on the patient is gathered, then examined to look for patterns and correlations. This procedure seeks to pinpoint a disease’s signs, stages, degree of harm, and many other things.
Next, projections about the patient’s condition are made using the predictive analytics model developed on top of data science. Additionally, it aids in formulating plans for the patient’s recommended course of therapy. Predictive analytics is a very helpful technology, and the healthcare sector heavily relies on it.
The following list outlines the main advantages of predictive analytics in healthcare:
- Chronic disease management is aided by it.
- It effectively tracks and examines pharmaceutical logistics demand.
- It forecasts a patient’s condition and recommends safeguards.
- It makes hospital data documentation quicker.
- It aids in effectively using medical staff and other resources to serve as many patients as possible.
- It foretells a patient’s upcoming medical issues.
Predictive analytics, a sort of data science, is therefore demonstrating its significant value in the healthcare industry.
Numerous bodily problems are intermittently appearing as a result of the expanding global population. This could be brought on by poor nutrition, psychological disorders, pollutants, physical ailments, etc.
Finding treatments or vaccinations for the diseases quickly has grown to be a problem for medical research institutions. It can take millions of test cases for researchers to comprehend the features of the causal agent to develop a formula for a drug. The researchers must then do additional experiments on the formula after discovering it.
In the past, it took 10–12 years to go through the data of the millions of test cases stated above. But it’s much simpler now thanks to a variety of Data Science applications in the healthcare industry.
It may take weeks or months to process the data from millions of test cases. It aids in assessing the drug’s effectiveness through data analysis. As a result, the vaccine or drug that has been successfully tested can be released in less than a year.
With the use of data science and machine learning, this is all achievable. Both have completely changed the way that the pharmaceutical industry conducts research and development. We’ll then explore how data science is used in the field of genetics.
Data Science in Genomics
One of the fascinating fields of study in medical science is genomics. It is the study of the sequencing and analysis of the DNA and genes that make up the genomes of living things. High-level treatments are facilitated by studies on organisms’ genes.
Discovering the traits and anomalies in DNA is the goal of genomics research. Additionally, it aids in establishing a link between the illness, its symptoms, and the affected person’s state of health. The investigation of medication response for a certain type of DNA is also a part of the research of genomics.
Before the development of effective data processing methods, studying genetics was a laborious and time-consuming task. This is because the human body has millions of pairs of DNA cells.
However, this process has recently become simpler because of the use of data science in the fields of healthcare and genetics. We can examine human genes more quickly and easily with the aid of various Data Science and Big Data techniques.
These techniques make it easier for researchers to identify particular genetic problems and the medication that works best for a particular type of gene.
The following are the resources utilized in genomics research:
- Large volumes of genetic data can be processed with the use of MapReduce. The processing of genetic sequences can be done more quickly with MapReduce.
- SQL: SQL facilitates the computation of genomic data as well as the retrieval of such data from multiple databases.
- Galaxy is a graphical user interface-based program used for biological research. We can use Galaxy to do specific procedures for genome research.
- Bioconductor: Bioconductors are employed in the genetic data analysis process.
Doctors can deliver the medication effectively if they are aware of how a patient’s DNA cells react to a specific medication. They can develop successful treatment plans to treat a condition for a specific patient thanks to the beneficial insights into the genetic structure.