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| 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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