We have all seen movies where a deadly virus infects the whole world and people start locking themselves in their homes for protection. Who knew this would become a reality? The coronavirus pandemic has forced all scientists and researchers to unite and figure out a way to change our reality.
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In this article, we will take a look at how machine learning methods for coronavirus are aiding this battle.
How are researchers using machine learning against coronavirus?
Machine learning is used to screen patients when they enter the hospital. They are given a face scan, which uses ML to detect whether they have a fever or not. All the screening data is stored and will be used later for diagnosis.
Wearable technology items such as Apple Watch and Fitbit use machine learning methods. ML is used for reading heart patterns and detecting possible heart issues. Recent research on FitBit data suggests that changes in resting heart rate can help in detecting influenza-like diseases such as COVID-19.
Similarly, OURA ring – a sleep and activity tracker – uses heart rate, breathing rate and your body temperature to detect COVID-19 progression and recovery rate. The device is powered by machine learning.
Research is in progress for both these devices.
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2. Detecting infection risks
The risk factors for contracting COVID-19 include the person’s personal hygiene, age, pre-existing conditions, human interactions, location, etc. Although it is still in its early stages, machine learning methods for coronavirus are used for developing a vulnerability index. This index specifies the likelihood of a person to contract COVID-19.
Machine learning approaches such as logistic regression and gradient boosted trees are used for this index.
3. Detecting viruses through proteins
A virus-host interactome is used to map the interactions between a host and a virus’s proteins. This map determines how the virus replicates in our cells and infects us. Machine learning models are being trained with protein data. This data has been used to predict HIV and H1N1.
These models are helping scientists to map the virus-host interactome faster. So, when they understand how the virus interacts with our bodies more clearly, drugs can be developed accordingly. This, in turn, has increased the speed of drug development and research.
4. Providing personalized treatments
On the basis of observational data about previous patients, machine learning methods are used for providing individualized treatments. The data is fed to the ML models so that they answer questions such as – “What is the appropriate time for putting a patient on a ventilator?”
Recent developments in the healthcare sector
Machine learning is being used in the healthcare industry to analyze huge datasets of patient information. By investigating the compounds and approved drug database using ML, researchers are trying to determine potential treatment solutions.
Recently, Andrew Satz and Brett Averso have launched a startup EVQLV, where they are using machine learning to aid their antibody discovery process. Discovering a proper antibody will help in fighting off COVID-19. They are developing ML algorithms to fasten the process. The algorithms reduce the possibility of drug discovery failure in the lab.
At Cambridge, the COVID-19 Sounds Project aims to collect health data using a mobile application. You have to provide your age, gender and record yourself coughing, breathing and reading loudly.
This data will be used to train machine learning models to predict if someone has contacted coronavirus.
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COVID-19 has brought all the researchers and scientists together. Machine learning methods for coronavirus are applied and studied thoroughly to find a way to end this pandemic. It is our responsibility to stay indoors and maintain personal hygiene while experts do their job.
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