‘HEALTH is WEALTH’ and Data science may help you get wealthier

I’ll start off by asking you a pretty simple question: how many doctors work in your area? How about five doctors? Now, let’s pretend there are 5-6 additional doctors to be exact. Have you ever taken a moment to estimate the number of people in your area and determine the ratio of doctors to possible patients (peoples) in the area?

OH!! Sorry, I must inquire ‘How many people’s are on each doctor?’ and because of the size of the number you will receive, it is impossible for doctors to observe even 10% of possible patients in a single day. As a result, you must make an appointment even as your condition deteriorates.

But the near future holds remedy to this situation.

How?!

Just count how many smart phones are available per people in your locality?

Living in this world it doesn’t take any special power to answer this question the ratio of smart phones over humans is approximately equal to 1. What if you simply opened your selfie camera and choose the “health status” filter, which displays your health state along with a highly accurate likelihood of having various diseases?

If I had told a human 50 years ago about the android phone, he would have laughed at me and never thought this to be true. But we are in a position where we can believe it because we are well aware of the magic of data science happening in this world of artificial intelligence.

‘A claim without an example is difficult to believe’, as wise men once stated.

I’ll provide you with multiple proofs so you can be sure.

The enormous impact of data science on business is well known because of its capacity to derive important insights from data while also keeping in mind that data can be related to things other than just business.

Data science in medicine is expanding quickly as a result of the digitalization of the healthcare system, which has produced an abundance of clinical big data. A study found that the human body produces 2 terabytes of data each day. This information covers heart rate, sleep patterns, brain activity, stress level, blood sugar level, and many other things. We now have BIG DATA in the game to handle such a big amount of data and DATA SCIENCE uses collected data to help monitor patients’ health.

Medical Image Analysis:

What if a doctor has 200 CT images to review in a single day? He will become frustrated and waste a lot of time that he could have used to study his patients. As we all know, quality often suffers when quantity increases, and as a human, there is a good chance that the accuracy of the analysis will deteriorate over time. However, machines are now capable of performing these tasks with a high level of speed and accuracy. Various imaging procedures for medicine include X-rays, sonograms, MRIs, CT scans, and many others. The doctors can treat the patients more effectively and more quickly if the images from these tests are properly and quickly analysed.

Yet, these are just standard image recognition techniques; the addition of data science has further revolutionized the healthcare sector with these imaging approaches. Deep learning has revolutionized image classification in the modern era. 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 diagnostic. Let’s examine the three popular algorithms for medical image analysis now:

•        The Anomaly Detection Algorithm: This system aids in spotting diseases including brain tumours and bone fractures, among others.

•        Image Processing Algorithm: Every image has some level of noise, and in addition to evaluating them, the image processing algorithm aids in improving and denoising the images.

•        Descriptive Image Recognition Algorithm: This algorithm visualises and collects information from photos, interprets it, and then uses it to create a more comprehensive picture (for example, merging the images of the brain scan and designating them accordingly)

Medical Predictive Analytics:

Do you go to the doctor if you feel well?

The possible answer is no and that’s natural because visiting a doctor costs you money and time so until we feel the urge to visit one we don’t. But, even if we feel well, a disease may be developing inside of us with a symptom that is not detrimental to us and we ignore it; however, when the disease advances, it causes us problems, and we then go to the doctor. The truth is that this procedure ultimately results in a serious health problem that, if caught earlier, could have been readily fixed.

If we use the patient data after an effective data collection process to train an appropriate machine learning (or deep learning) model, it can produce a highly accurate disease detector. These techniques are used to examine numerous patterns and correlations in the data and can be used to determine a disease’s phases, symptoms, and more. It aids in the effective use of medical professionals and other resources for the benefit of the greatest number of patients and additionally forecasts a patient’s upcoming medical crisis.

Next, the predictive analytics model built on top of data science then forecasts the patient’s state. Also, it aids in formulating plans for the proper course of treatment for the patient. Predictive analytics is therefore a very valuable method and it is very important to the healthcare sector.

Drug Discovery and Effective Drug Development:

The increasing population in the modern world is undoubtedly a pain for the governments, but the rising number of diseases is currently the most alarming issue. 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 in order to develop a formula for a drug. The researchers must then do additional experiments on the formula after discovering it. The foundation for artificial intelligence-assisted medication synthesis is what data science contributes most to the pharmaceutical sector.

Formerly, it would have taken 10–12 years to go through the data of the millions of test cases stated above. But, it has now significantly gotten easier thanks to different Data Science applications in the healthcare industry. Processing the data from millions of test cases could take weeks or perhaps just a few months. Via data analysis, it aids in determining the drug’s efficacy. As a result, it won’t take more than a year to release the vaccine or medication that has been successfully tested. Machine learning algorithms can determine if a drug will have the intended effect on a person’s body by using case study reports, lab test results, previous medical data, and the effects of the pharmaceuticals in clinical trials. Behind the development of covid-19 vaccines there was also a pivotal role of data science.

Virtual assistance:

Your Smartphone functions as a virtual doctor in this case.

Applications created using virtual aid are an excellent illustration of how data science is used. To provide patients with individualized experiences, data scientists have created extensive platforms. By examining the symptoms, data-driven medical applications help patients diagnose their illnesses. The application will diagnose the patient’s health and ailment once the patient enters just a few symptoms. Depending on the patient’s health, it will advise on safety measures, medications, and the appropriate course of action.

Additionally, the application analyses the patient’s data and generates a checklist of the necessary steps in the therapy process. The patient is then routinely informed to take their medications. This aids in preventing situations of carelessness that can aggravate the disease.

Also, people with Alzheimer’s, anxiety, sadness, and other psychiatric problems have found virtual aid to be helpful. As a result of the application’s regular reminders to take the necessary actions, these patients’ treatment becomes more effective. This includes taking the right medications, exercising, and eating the right things. Woebot, a virtual assistant created by Stanford University, is one example. It is a chatbot that assists people with psychological illnesses in getting the right care to improve their mental health.

Looking at all these uses of data science in healthcare, we may conclude that it is one of humankind’s most amazing inventions.

Monitoring Patient Health via Wearables:

The Internet of Things (IoT), a modern phenomena that guarantees maximal connectivity, is a boon to data science. Now that this technology is being used in medical field, it can assist in keeping track of patients’ health. Currently, people track and manage their health with smartwatches and fitness trackers. Furthermore, if given access, a doctor can monitor the patient’s condition using these wearable sensor devices and, in severe circumstances, remotely treat the patient. The use of data science technology can help identify even small changes in a patient’s health markers and anticipate potential problems. A patient’s current status is used by a variety of wearable and home devices connected to an IoT network to anticipate whether they will experience any problems in the future.

Applied Genomics Data Science:

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 DNAs is the goal of genomics research. Also, 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.

Prior to 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. Yet, this process has recently become simpler because to 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 utilised in genomics research:

•        MapReduce: This programme facilitates the processing of enormous volumes of genomic data. 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 diverse databases.

•        Galaxy: This graphical user interface-based programme is used for biomedical research. We can use Galaxy to do specific procedures for genome research.

•        Bioconductor: Genetic data analysis is done using bioconductors.

Doctors can deliver the medication effectively if they are aware of how a patient’s DNA cells react to a specific medicament. They can develop efficient plans to treat a patient’s ailment thanks to the beneficial insights into the genetic structure.

Recognizing risks associated with the patient:

Data scientists can develop machine learning algorithms that incorporate factors like socioeconomic status, test results, and other personal data to produce results of a person’s health status. A scientist can resolve the issue of predicting stroke patterns using support vector machines. One of the key applications of data science in healthcare calls for a thorough examination of exploratory data as well as the usage of algorithms.

Data science in healthcare has several advantages, some of which are given below:

•        Less treatment failure thanks to precise predictions and recommendations: This is perhaps the most crucial application of data science in healthcare. When the data scientists gather a sizable amount of information about the patient’s medical history, the stored information can be used to recognise illness symptoms and make a precise diagnosis. Mortality rates have significantly decreased since treatment options may now be tailored and care is given with better knowledge.

•        Creates a specific skill set: Accurate diagnosis-making abilities must be developed in order to deliver high-quality care. By using predictive analytics, one may identify which patients are more at danger and figure out how to act quickly to stop significant harm. Data science can be a solution for the enormous amount of data that needs to be managed with competence to prevent administrative errors.

•        Reduces healthcare costs: Medical data scientists can utilise Electronic Health Records (EHRs) to discover patients’ health patterns and avoid needless hospitalisation or treatment, which lowers costs.

Future of Data Science in Healthcare:

Fundamentally, there are four things that are causing the healthcare sector to advance quickly:

•        Technological progress

•        Digitalization

•        The need to shorten the length and expense of therapy

•        The need to manage a big population

To get the intended result, data science has already begun tackling all of these. Since data science is already benefiting society, its use in the future will undoubtedly prove to be even more helpful. It will raise the bar for the healthcare sector. Patients will benefit from more individualised care and excellent therapies, while doctors will receive a lot of support.

To sum up, data science applications in the healthcare sector have the potential to improve the overall healthcare system.