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Big Data Analytics 2020- The end of 2020 marks the conclusion of one of the most formidable years the healthcare industry has seen in recent memory.
The COVID-19 pandemic brought new challenges with it, while also shining a harsh light on longstanding issues. Leaders acted quickly to leverage big data analytics tools, including AI and machine learning, to make sense of the virus and control its spread, resulting in a year of technological achievements and rich data resources.
In a list of the top ten stories from the past 12 months, HealthITAnalytics describes the events and trends that dominated readers’ attention. While many will be glad to see 2020 go, a look back on some of its major incidents indicates that the crisis sparked innovations that will live on long after the new year.
10. WHITE HOUSE URGES AI EXPERTS TO DEVELOP TOOLS FOR COVID-19 DATASET
Soon after the Trump administration declared COVID-19 a national emergency, officials sought the help of big data analytics tools to better understand virus transmission, risk factors, origin, diagnostics, and other vital information.
The White House Office of Science and Technology Policy issued a call to action for experts to develop artificial intelligence tools that could be applied to a COVID-19 dataset – the most extensive machine-readable coronavirus literature collection available for data mining at that point.
The call to action showed leaders’ confidence in the potential of AI, and foreshadowed the critical role advanced analytics tools would play in mitigating the impact of the pandemic.
9. AI SHOWS COVID-19 VACCINES MAY BE LESS EFFECTIVE IN RACIAL MINORITIES
With the FDA recently granting emergency use authorizations for new COVID-19 vaccines, many people in the US are looking forward to the beginning of the end of the pandemic.
However, as this MIT study showed, these vaccines may not be the all-encompassing solutions they’re believed to be.
Researchers used an artificial intelligence tool to examine a kind of vaccine similar to COVID-19 vaccines and found that it could be less effective in people of black or Asian ancestry. The results further emphasize the stark racial and ethnic disparities that have been consistently highlighted throughout the pandemic.
8. BIG DATA ANALYTICS CALCULATOR DETERMINES COVID-19 MORTALITY RISK
As the pandemic has worn on, public health officials are continually searching for innovative tools to help allocate resources and guide decision-making. A team from Johns Hopkins School of Public Health leveraged big data analytics to develop a COVID-19 mortality risk calculator, which could inform public health policies around preventive resources, like N-95 masks.
The risk calculator could also help allocate early vaccines, acting as a companion to guidelines from other organizations and ensuring that the right people are vaccinated first.
7. GOOGLE MAKES COVID-19 DATASETS FREELY AVAILABLE TO RESEARCHERS
The onset of COVID-19 sparked a new wave of data sharing and access in healthcare. In late March, Google Cloud announced that it would offer researchers free access to critical coronavirus information through its COVID-19 Public Dataset Program, which aims to accelerate analytics solutions during the global pandemic.
The program will make a hosted repository of public datasets free to access and query, including the Johns Hopkins Center for Systems Science and Engineering (JHU CSSE) dashboard, Global Health Data from the World Bank, and OpenStreetMap data.
6. DATA SCIENTISTS USE MACHINE LEARNING TO DISCOVER COVID-19 TREATMENTS
Early in the pandemic, researchers were working to discover potential therapies for COVID-19 using AI and machine learning tools. Two graduates from the Data Science Institute at Columbia University launched a startup called EVQLV that creates algorithms capable of computationally generating, screening, and optimizing hundreds of millions of therapeutic antibodies.
Using this technology, the pair aimed to discover treatments that would likely help individuals infected by the virus that causes COVID-19. The machine learning algorithms are able to rapidly screen for therapeutic antibodies with a high probability of success.