Data Science in Healthcare– Know The Hidden Scopes
Data science in Healthcare isn’t something new. It is the most common industry where data science and analytics are applied. The global pandemic has dramatically raised the demand and importance of healthcare data scientists. Over the years, we have seen how data science professionals have pulled together to work on Covid-19 Healthcare data and build AI/ML models to track the outbreak. This data was used for contact tracing, screening, and vaccine development.
Thus, data science has the potential to improve the entire healthcare system.
Maybe you’ve worked in healthcare for a while and want to shift your career path to put your analytical skills to the test. Or perhaps, you have strong experience in data analysis and are seeking a field where you can put your knowledge and expertise to use. Even, the case might be that you are not happy with your current career growth in the healthcare industry and dreaming of a lucrative package like your IT friends. Believe me, that’s also possible and the key is nothing but data science and AI.
In either case, you are considering whether a career as a healthcare data scientist would be the right fit for you.
This blog will help you familiarize yourself with everything you need to know as before/ after stepping into the healthcare industry.
- What role does data play in the healthcare sector?
The healthcare industry contributes a substantial amount of data to the global data pool
Data science and AI have the potential to transform how care is delivered.
Today Medical Science has advanced rapidly, increasing life expectancy around the world. However, as longevity increases, the healthcare system faces a growing demand for their services, rising expenses, and a workforce struggling to meet the requirements of their patients. - 5 Major Applications of Data Science in Healthcare
Data Science has already begun to address all of these issues in order to achieve the desired result. As Data Science is now benefiting society, its applications undoubtedly will be more valuable than ever. It will propel the healthcare business forward. Doctors will have a lot of assistance, while patients will have a more personalized experience and treatments. Let’s look at some of the essential applications in healthcare:
- Predictive analysis: The predictive analysis model generates predictions about a patient’s status. It analyzes various correlations between symptoms, behaviors, and diseases and generates relevant predictions. It also enables healthcare to build predictive models using data science which in turn makes it possible to identify potential risks before they arrive.
- Medical image analysis: Medical imaging is the most common application of the data science healthcare industry. Techniques like X-ray, MRI, and CT scan reveal the inner parts of the human body. With the advent of deep learning, it is now possible to identify the defects in the human body and help doctors in developing successful treatment options.
- Drug Discovery:Today, pharmaceutical industries rely significantly on DS to provide better drugs for patients. They make use of patient information like mutation profiles and metadata to derive insights which in turn helps in the development of models.On the other hand, it is possible to improve drug discovery procedures by collecting historical data.
- Genomics: Before the development of data analysis techniques, genomic research was a time-consuming task. But today Data science in healthcare has made it effortless and easier. Researchers now use DS to study DNA sequences to discover the link between the parameters within it and the diseases transferred by genes.
Monitoring patient health: IoT gadgets are being used by certain patients and clinicians as wearable monitors to track heartbeat and temperature. Data Science in healthcare collects data and analyzes it with the help of data science. Using analytical tools doctors can monitor a patient’s blood pressure, circadian cycle, and calorie intake.
- What is the Role of a Data Scientist in Healthcare?
Data scientists in healthcare help in exploring ways to predict drug behavior and gain a better understanding of human diseases.
The primary role of a Healthcare data scientist is to apply all data science techniques to healthcare software and applications. They draw meaningful insights from data to make predictive models.
In general, the core responsibilities of a healthcare data scientist are as follows:
- Performing data analysis with various analytical tools.
- Collection data/ health data.
- Analyzing hospital requirements.
- Organizing and sorting data for use.
- Implementing algorithms to extract insights.
- Building predictive models with the development team.
- Database management including data collection, retrieval, storage, and security.
- Converting data into easily digestible chunks for non-technical employees of an organization.
- Understanding hospital procedures and systems, as well as utilizing data to aid decision-making.
- Performing information-based audits.
- What skills are required to become a successful data science professional in Healthcare?
The popularity of big data and its potential impact on the healthcare industry has driven the demand for more qualified data scientists.
There isn’t a “one-size-fits-all” approach to data science. Instead, each role is unique, and because data scientists are in high demand across the healthcare industry, these jobs can require a wide range of talents.
It is important to build a strong foundation of skills before moving forward based on your interest and strength.
- Mathematics and statistical skills
- Programming languages, Python and R
- Database management languages like SQL and SAS
- Machine Learning and deep learning concepts
- Data visualization
- Quantitative skills and analytical skills
- Communication and presentation skills
While these skills may help data scientists in analyzing the huge amount of data, healthcare data scientists are great at problem-solving and storytelling and are aware of their organization’s objectives. They need to discuss how to leverage data and insights with other data professionals, interact with laboratory staff and also engage with patients.
- Healthcare data scientist and data analyst salaries: What to expect?
Today, Healthcare organizations are investing heavily in data science and analytics since it helps them in reducing administrative costs, minimizing fraudulent payments, delivering more accurate treatments and diagnostics, and overall decision making.
Like any other industry, the salary of a healthcare data scientist is typically determined by qualification, skill set, experience, location, and organization.
On average, The annual salary of Data scientists in Healthcare and life science companies is expected to be around Rs. 40 LPA.
Some of the popular life science companies are as follows:
Healthcare data analyst Salary:
According to Glassdoor, In India, the average income for a Healthcare Data Analyst is Rs. 7,61,298.
- Data Science in Healthcare Projects ideas to level up your portfolio:
The majority of data science expertise is gained through data science projects which help in deeper understanding, greater retention, and awareness of real-world problems faced by data scientists. So, If you want to become a data scientist, your first step would be to learn how to work with data and then work on data science use cases.
Here are some project ideas you can work on to level-up your portfolio:
- Medical image segmentation: Medical image segmentation is the process of extracting areas of interest from 3D image data, such as Magnetic Resonance Imaging (MRI) or Computerized Tomography (CT) scans. The main purpose of this project is to identify the areas of anatomy required for a particular investigation.
Image segmentation dataset by Kaggle
- Ultrasound nerve segmentation: It is very crucial to accurately identify the neural structure in ultrasound images before inserting the patient’s pain catheter. In this project, you’ll learn how cutting-edge deep learning techniques are utilized to develop an end-to-end system where a person just feeds on an ultrasound image of the region to a deep learning model where it segments the nerve seen in the image.
Ultrasound Nerve Segmentation dataset from Kaggle
- Heart failure prediction: Heart failure is a common consequence of cardiovascular diseases (CVDs) resulting in an increase in mortality rate. In this project, you’ll build a Machine Learning model that predicts mortality by heart failure. Throughout this project, you’ll learn multiple ML algorithms including Random Forest and K-NN, data wrangling, and filtering techniques.
Heart failure prediction dataset from Kaggle
- Depression, anxiety, and stress prediction: Depression and stress Detection is the challenge of identifying signs of depression in individuals. This sign may be identified in several behavioral changes in a person. These symptoms can be predicted by developing a model with AI and ML algorithms such as CNN, support vector machine, KNN classifier, and linear regression.
Depression analysis dataset by Kaggle
- Breast cancer prediction: Breast cancer affects approximately 12% of women worldwide and is on the verge to rise even more. This project helps doctors to predict whether a patient has breast cancer or not. You’ll be required to create an ML model to classify malignant and benign tumors by utilizing the supervised Machine Learning classifier technique.
Breast cancer prediction dataset by Kaggle
The first crucial step in becoming a healthcare data scientist is earning a Bootcamp certification or a master’s degree that gives you the expertise and skills to succeed.
Most people consider self-study or enrolling in a Bootcamp.
Learnbay is a well-known institute for learning data science concepts. They offer affordable, flexible, and beginner-friendly Data science and AI courses in Bangalore_ and globally as well. You’ll gain knowledge of advanced DS and AI tools and concepts that are effectively used in the Healthcare domain. Their case studies and industry projects in healthcare will help you stand out from the competition.
Conclusion
By now, you’ve seen that DS has revolutionized the healthcare industry in large ways and how Healthcare and pharmaceutical industries have heavily utilized data science and AI to improve patient lifestyles. I hope this blog helped you understand the value of domain expertise in healthcare.
Also, Keep in mind that the pharma and healthcare industry will never be redundant since it is an integral part of human life. Hence, this is the perfect time for you to make that career move you have always wanted.
Wake up and begin your journey in data science and AI from Learnbay institute!
For more such content, do check out our site: Learnbay
You can subscribe to our social media channels to get regular Data science and AI updates.
Twitter: https://twitter.com/Learnbay
Facebook: https://www.facebook.com/learnbay/
