Data Science in Healthcare

        Data science is an interdisciplinary field that uses scientific methods, data mining techniques, machine learning algorithms, and big data to extract knowledge and insights from many structured and unstructured data. The healthcare industry produces large datasets containing useful information about patient demographics, treatment plans, health care results, insurance, and more. The data collected by Internet of Things (IoT) devices has attracted the attention of data scientists. Data science supports the processing, management, analysis, and assimilation of large amounts of fragmented, structured, and unstructured data created by healthcare systems. This data requires effective management and analysis to produce factual results. This article reviews and describes the data cleaning, data mining, data preparation, and data analysis processes used in healthcare applications. This article provides insights into the status and perspectives of big data analytics in healthcare, presents benefits, outlines the frameworks and techniques used, outlines current challenges, and provides feasible solutions. Data science and big data analysis provide actionable insights and help make strategic decisions in the healthcare system. Helps to get a comprehensive view of patients, consumers and doctors. Data-driven decision-making opens up new opportunities to improve the quality of health care.

        According to a study, the data generated by every mortal body is 2 terabytes per day. This data includes conditioning of the brain, stress position, heart rate, sugar position, and numerous further. To handle such a large quantum of data, now, we've more advanced technologies and one of them is Data Science. It helps cover cases ’ health using recorded data. With the help of the operation of Data Science in healthcare, it has now come possible to describe the symptoms of a complaint at a veritably early stage. Also, with the arrival of colorful innovative tools and technologies, croakers are suitable to cover cases ’ conditions from remote locales. In earlier days, croakers and sanitarium operations weren't suitable to handle multiple figures of cases at the same time. And due to the lack of proper treatment, the cases ’ conditions used to get worse.

source: Technology Networks

How data science is playing major role in healthcare

        The healthcare industry produces large sets of useful information about patient statistics, treatment plans, medical examination results, insurance,

etc. Data collected from Internet of Things (IoT) devices is attracting the attention of data scientists. Data science provides assistance in processing, managing, analyzing, and integrating large amounts of different, structured, and informal data created by health care systems. This data needs to be successfully managed and analyzed in order to get real results. The process of data purification, data mining, data processing, and data analysis is used in health care systems.Data science and extensive data analysis can provide practical insight and assistance in strategic decision-making regarding the health system. It helps to build a holistic view of patients, clients, and therapists. Data-driven decision-making opens up new opportunities to improve the quality of health care.

Ultimate goals of data science

The ultimate pretensions of the healthcare system are as follows:

To ease the workflow of the healthcare system

To reduce the trouble of treatment failure

To give proper treatment on time

To avoid gratuitous extremities due to thenon- vacuity of croakers

To reduce the waiting time of cases

Data Science for Medical Imaging

source: Frontiers

        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. However, it was often difficult to find microscopic deformities and as a result, doctors could not suggest a proper diagnosis.

        With the advent of deep learning technologies in data science, it is now possible to find such microscopic deformities in the scanned images. Through image segmentation, it is possible to search for defects present in the scanned images. Other than this, there are also other image processing techniques like image recognition using Support Vector Machines, image enhancement and reconstruction, edge detection etc.

Automated lab work

        Lab automation can help the scientists to predict the result or diagnosis of experiments in the healthcare domain more accurately, and it also saves time. It also helps to centralized all the history and all the data of the previous experiments. The centralizing of the data can help to access the important data from anywhere. It also helps the doctor to create optimized and crisp reports. Scientists can store all the data in all the steps of the experiments in real time. This way, we can say that Automated lab experiments and tests help a lot in healthcare domain.

Wearable Devices

        Many multinational companies are focussing on the health monitoring wearable devices. The data collected from wearable devices can help predict some future threats about the person wearing those wearable devices. The data of steps walked, pulse-rate, blood pressure, etc. this data can help monitor the patien’t overall health and well-being. 

Disease surveillance

Pandemic diseases are mostly the contagious, therefore the data from previous spread of disease could be used to prevent the things in the next wave of contagious diseases. The data of previous pandemics could be used to predict the spread of any upcoming epidemic, pandemic including AIDS, Influenza, respiratory diseases and recent wave of COVID-19 pandemic. Data science can also come in the picture while distribution of the vaccines and the medication. It keeps the data of supply chain management, which can make the medication more approachable.

Drug-drug interaction

Drug-drug interaction includes relation between multiple experiments. Thus Data Science could really help in analyzing the interrelation of the drugs and their effect on human body. Neural networks can be used to predict the drug-drug interaction.

Data Science for Genomics

        Genomics is the study of sequencing and analysis of genomes. A genome consists of the DNA and all the genes of the organisms. Before the availability of powerful computation, the organizations spent a lot of time and money on analyzing the sequence of genes. This was an expensive and 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 in a much lower cost.The goal of research scientists is to analyze the genomic strands and search for irregularities and defects in it. Then, they find connections between genetics and health of the person.researchers use data science to analyze the genetic sequences and try to find a correlation between the parameters contained within it and the disease. 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.Bioconductor is an open-source software developed for the analysis and comprehension of genomic data.

Drug Discovery with Data Science

        Drug Discovery is a highly complicated discipline. Pharmaceutical industries are heavily relying on data science to solve their problems and create better drugs for the people. Drug Discovery is a time-consuming process that also involves heavy financial expenditure. Data science is revolutionizing this process and increasing the success rate of predictions. 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.it is now possible to improve the collection of historical data to assist in the drug development process.

Predictive Analytics in Healthcare

        Healthcare is an important domain for predictive analytics. A predictive model uses historical data, learns from it, finds patterns and generates accurate predictions from it. With data science, hospitals can predict the deterioration in a 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. Predictive analytics plays an important role in monitoring the logistic supply of hospitals and pharmaceutical departments.

Monitoring Patient Health

        The IoT 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 blood pressure as well as their calorie intake. For patients that are chronically ill, there are several systems that track patient’s movements, monitor their physical parameters. It makes use of real-time analytics to predict if the patient will face any problem based on the present condition.  

Tracking & Preventing Diseases

        Data Science plays a pivotal role in monitoring patient’s health and notifying necessary steps to be taken in order to prevent potential diseases from taking place. Data Scientists are using powerful predictive analytical tools to detect chronic diseases at an early level. There are several instances where AI has played a huge role in detecting diseases at an early stage.

Benefits of Data Science in Healthcare

        Science helps in advancing healthcare installations and processes. It helps boost productivity in opinion and treatment and enhances the workflow of healthcare systems. The ultimate pretensions of the healthcare system are as follows

To ease the workflow of the healthcare system

To reduce the trouble of treatment failure

To give proper treatment on time

To avoid gratuitous extremities due to thenon- vacuity of croakers

To reduce the waiting time of cases

Disadvantages

Big data has many advantages, but you need to consider the disadvantages before you take the plunge.

Privacy

        One of the biggest drawbacks of big data is the lack of privacy, especially when it comes to sensitive medical records. To provide an effective, complete and comprehensive view of the patient, big data needs access to everything, including private records and social media posts. According to many big data experts, technology is robbing personal privacy for greater benefit.

Replacing Doctors While some have a positive view of their ability to predict future medical problems, big data also risks replacing doctors. Big data is simply not ready to be used on its own and definitely lacks the personal feel of a human doctor. Some experts are concerned that the increase in big data could undermine doctors and cause patients to seek answers from technology rather than licensed doctors.

Future scope

        Data has completely altered the healthcare industry within the last 10 years. New technologies, like Electronic Medical Records (EMRs), allow doctors to research statistics on a bigger number of patients without manually inputting the info. Whereas in future AI (AI) will play a big role within the healthcare industry. As an example, the sphere of AI-enabled clinical decision support is simply emerging. this kind of support can compare patients who fit similar profiles within a system, then it can alert doctors to trends in data that will are overlooked. The employment of huge data in healthcare will include testing for drug interactions that little studies are unlikely to catch and forestall patients from taking harmful drug combinations. Decisions made by physicians, like what test or treatments to allow a specific patient, form up 80-90% of all healthcare spending, so using computing to form more educated decisions will bring down healthcare costs. It’s crucial to own informed leaders at the vanguard of those innovations in healthcare.

Basically, there are four factors resulting in rapid improvement within the healthcare industry:

Technological advancements 

Digitalization 

Need for reducing treatment costs and duration

Need for handling large population 

Data Science has already started addressing these to bring the required effect. As Data Science is already doing wonders for society, there's little question that its application within the future will prove itself to be more invaluable. it'll take the healthcare industry to further heights. Doctors will get ample assistance and patients will get a more personalized experience and excellent treatments.

Conclusion

        Therefore, we can conclude that data science has many applications in health care and that the medical and healthcare industry has made extensive use of Data Science to improve patients' quality of life and early diagnosis.

        In addition, with advances in medical imaging, it is possible for doctors to find small tumors that were previously difficult to diagnose. Therefore, data science has transformed health care and helped the medical industry in many ways.