Promises and Challenges of Machine Learning and Data Science in Health

July 22, 2020 09:05:04

You may have heard about machine learning and data science in different aspects of life, such as in how your insurance premiums are determined. But, did you know that machine learning and data science are playing an increasing role in public health? Read on and learn about this exciting development.

What is Big Data?

It refers to the massive amount of information, such as data from administrative health claims and biobanks, which is available to researchers in a de-identified fashion. Furthermore, it is termed as significant due to the undiluted number of people represented in hundreds of millions and also the massive amount of information of the people involved.

It contains vital information, such as their genomes and postal codes. The primary function of these datasets is not always research but for other purposes such as billing. However, one should always ask this natural question, “Are these sets of information useful for public health surveillance and health related discoveries?”

Promises of Machine Learning and Data Science in Health

Machine learning and data sciences are one of the surest ways of summering big data. Besides, the process will predict and validate the data patterns, thus making sense to both doctors, policymakers and patients. Machine learning will also analyze a complicated relationship between variables and find links between environmental or clinical factors and risk for disease.

Secondly, machine learning will also lead to improvement and accuracy in disease diagnosis from medical image analysis. For example, it leads to an improvement in automatic screening for diabetic retinopathy. Google and its collaborators have also proven that machine/deep learning can easily automate image analysis at the highest accuracy levels. Furthermore, it has also been compared to the very best physician examiners in its working processes.

Machine learning has also led to the integration of data types for better understanding of complex associations between environment, genetics and disease. For instance, Harvard University has used large datasets in trying to untangle the relationship between environment and genetics in all diseases. The study has been aided by machine learning due to the large amount of data recorded in health insurance.

Besides using biobanks, machine learning has also been used by scientists in discerning new genetic variants such as environment-wide association studies and genome-wide association studies. The studies have been conducted to help in identifying novel associations with a high-risk diseases that might be missed while studying one at a time.

Challenges Associated with Machine Learning

Machine learning may lead to limited generalization, confusion and even complex correlations between variables. Besides, new modelling using machine learning may not be easily explained by policymakers and physicians. Analysts may be faced with various challenges of the variables to model and which one to be ignored. These variables are always arbitrary and may lead to different interpretations and findings.

You can bet that companies like Genprex Inc. (NASDAQ: GNPX) are using machine learning and data science in their operations in order to bring better products to market.

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