COVID-19 epidemic impacts mental health, Here is how ML can help

Srivatsan Aravamudan Nemili / Mar 17, 2020

Covid-19 affect mental health , Machine Learning can help
How Machine Learning can help in Mental health crisis


Covid-19 has claimed lives, closed borders and house arrested millions of people around the globe. From toilet paper panic buying to livelihood uncertainties the effects of the Coronavirus epidemic are only growing day by day. At this point, there is no escape from the effects of the virus. Whether we are infected directly from it or not, our way of life has changed dramatically over the last few weeks.
While many of us are getting adjusted to the new norms of life, one least looked area of the impact is mental health. Business owners are worried about the uncertainty and collapsing market. Employees are worried about not getting paid and not being able to support their families.  Some cant get work from home option and so they have a tough time finding alternatives for babysitting.  

We can’t deny the fact that this outbreak has created stress in a lot of people. Fear and anxiety have taken over. While young adults are passing memes and discussing discomforts of being at home, adults either afraid about their own health or that of their loved ones.  Being isolated and at home has increased sleeping and eating disorders.

When tragedy like this strike, we meet our friends and family, go to prayers or play sports. We hug, touch and comfort people. With the Covid-19 outbreak, we can’t gather in churches, meet our friends, play sports, have drinks, have gatherings or have physical contact to comfort ourselves / others.  On the other hand, we are constantly bombarded with news about the challenges and hardships of the outbreak.  Mental health is going to one of the priorities for all of us as we sail through this hardship.

Machine Learning in diagnosing mental health


AI has taken over several industries including different verticals of the health industry. Machine learning a subset of AI is showing good signs in the subjective diagnosis of mental health. ML even supersedes human capabilities in accurately identifying the disorders.

There are no blood tests for mental health, and often humans can miss out on the cues of the patients such as the words they use, which can be symptoms of mental health. This is where ML can play a major role as they are good at identifying subtle cues, changes in day to day speech, etc. This is exactly a team of researchers from the University of Colorado Boulder are doing.  Using Machine learning in psychiatry, they created a speech-based mobile app that can categorize a patient’s mental health status as well as, or better than, a human can.

Researchers from the World Well-Being Project (WWBP) analyzed social media with an AI algorithm to pick out linguistic cues that might predict depression. Although these are at the early stages of addressing the tremendous need for the mental health care of this situation, it provides a good starting point. It also enables us to exploit our resources and create such applications to address the needs of the crisis.



Srivatsan Aravamudan - Sri


Senior Solution Consultant

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