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4 min read

How Data Science Is Being Used In Healthcare

data science in healthcare

As technology has made data increasingly accessible, data science continues to transform and shape how we think about problems across various industries. Healthcare has a vast number of applications for predictive and prescriptive analytics as data flows in through EMR’s, insurance claims, patient data, genetic information, clinical trials, and even social media. Regardless of where your organization sits, data science can help you solve a number of problems. Today we will be taking a look at a few use cases for data science in the healthcare industry.

Applications of Data Science:

Diagnosis

The diagnosis of patients through medical imaging is one of the more popular applications of data science. Computers are learning to more accurately interpret MRI’s, X-Rays, CAT scans, and many other types of images. As machines can detect microscopic deformities, they are ideal in identifying anomalies that would otherwise go undetected.

In the images below, we can see the use of deep learning (deep neural networks) in Magnetic Resonance Imagen (MRI) segmentation, disease detection, and disease prediction. The images demonstrate the use cases for organs such as the brain (highlighting areas of activity), kidney (predicting segmentation), and the prostate and spine (zooming in/ highlighting for disease detection and prediction). 

Brain

brain scan

 

Kidney

kidney scan

 

Prostate

prostate

 

Spine

spine

Image Source: An overview of deep learning in medical imaging focusing on MRI

Data science has the potential to save billions of dollars through the proper diagnosis of medical images.

Genomics

Genomics is an additional area of healthcare that has a lot of applications for data science. Gene therapy is an emerging field in which we can insert genetic materials into cells to treat abnormal genes, and we can use genomics to better understand, diagnose and treat patients as individual cases. 

Chronic daily headaches (CDH) and chronic migraines (CM) are some of the most frequent problems encountered in neurology. These headaches are often complicated by medication-overuse headaches (MOH). Using data science techniques, we can help understand genomic expression patterns in medication-overuse headaches that respond to the cessation of the overused medication. We do this by implementing hierarchical cluster analysis groups (see image below)- identifying and grouping the subjects and probesets that are most alike together.  This gives us insight into how individuals may respond to their medication. 

gene imaging

Image source: Genomic expression patterns in medication overuse headaches

It's estimated that gene therapies can save more than $33B in the next ten years.

 

Management Analytics

With the vast amount of data collected, data science techniques can be employed to correlate and associate symptoms and detect disease. Organizations like hospitals can use these techniques to engage and improve patient management. One potential application would predict hospital readmissions based on the previous history of patient profiles. This allows hospitals to proactively manage patients so readmissions occur less frequently. Data science can also be used to identify methods that cut patient waiting times, optimize hospital locations, and even build infrastructure to help doctors keep track of patient profiles. Other management optimization opportunities include adjusting staff assignments at certain hours to maximize efficiency, or managing hospital beds to satisfy patient demand. Data science techniques can also be deployed to predict lab tests, identify patients who are at risk for financial difficulties, and can predict fraud attempts.

It is important to correctly classify each exam of Interventional X-Ray (IXR) systems into their respective procedures and assign them to the correct anatomy. This classification enhances the productivity of the system and can lead to better scheduling in the catheterization laboratory, where imaging typically takes place. It also provides means to perform device usage and revenue forecasts of the system by hospital management, and allows the organization to focus on targeted treatment planning for a disease. For example, machine-learning techniques (SVM, KNN and decision trees) on log information of the systems were applied and showed an accuracy of greater than 90%. Source: A machine learning framework for auto classification of imaging system exams in hospital setting for utilization optimization

 

Drug Discovery

Organizations that are involved in drug discovery are tasked with deconstructing human data and building medicine through mutation profiles and patient metadata. With this information, data science techniques are deployed to build models that find statistical relationships between attributes. By understanding this data, drugs can be developed to address genetic sequences and mutations.

Source: Machine-learning approaches in drug discovery: methods and applications

Big data and machine learning in pharmaceuticals and medicine could generate a value of up to $100B, annually. Data science can save up to 20% of the costs of running clinical trials (over $58M in value). 

 

Disease Mapping and Prevention

It’s hard to believe that it has been over a year since the beginning of the COVID-19 pandemic. Data science techniques played an important role to model the growth of the pandemic, identify hotspots, and even track the spread of the disease. Data science also played a huge role in increasing our understanding of the virus. Clustering methods were used to understand COVID-19 spread rates across different countries, CT scans mapped neural networks to identify areas of activity in those diagnosed with COVID-19, and Recurrent Neural Networks(RNN) were used to understand the rapid growth of the virus in predicting the future spread of the pandemic.

Source: Significant applications of machine learning for covid-19 pandemic

Similarly, data science is being used to track Ebola across Africa to prioritize sending aid workers and doctors to the most impacted areas, saving countless lives. It's estimated that the use of data science methodologies could save 6 million lives from preventable death by 2030.

 

Conclusion

Although this blog has reviewed a few high-level concepts and applications of data science in the healthcare industry, the opportunities are limitless. The foundation of a good model is almost always grounded by a question.

If you have questions, there's a high possibility that data science can be used to help facilitate your business!

Reach out to DataDrive today if you're interested in a strategic assessment for your business! 


Additional References:

Data Science and AI in Drug Discovery and Development

Ten simple rules to power drug discovery with data science How Is Machine Learning Accelerating Drug Discovery? 

How Data Science is Fighting Disease

How data science could save 6 million lives from preventable death by 2030

Disease mapping

Outbreak analytics: a developing data science for informing the response to emerging pathogens 

Deep Learning Market: Focus on Medical Image Processing 

New Report Demonstrates Potential for Cell and Gene Therapies to Provide Cost Savings 

Why Hospitals Need Better Data Science

 


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