Hi everyone, today
We're going to be talking about how artificial intelligence can
be used to track the next pandemic.
Artificial intelligence also known as AI is defined by the
Oxford dictionary.
As the development of computer systems,
able to perform tasks that normally require human intelligence such as
visual perception, speech recognition, decision-making, and translation between
languages. To simplify, AI is a computer program that is capable
of performing a task,
which requires intelligence.
This task is usually something
a human can accomplish such as learning,
planning, and problem solving.
Artificial intelligence uses
machine learning to give computers the ability to learn without
explicitly being programmed.
Machine learning is a subset of artificial intelligence. It employs
algorithms that learning from data to make predictions or decisions
that allow performance to improve when exposed to more data
over time. An algorithm is a set of instructions for solving a problem
or accomplishing tasks.
Machine learning starts with data such as numbers photos
or text. The data is then gathered and
used to train the computer system.
the more data, the better
the AI model.
From there,
the computer system,
trains itself to find patterns, make predictions and create
algorithms.
Learning is a subset of machine learning and closely mimics.
the way a human brain works. It uses many layers of neural
networks to create algorithm to find the best way
to perform tasks on its own.
It analyzes data
similarly
to the way a person would look at a problem.
Deep learning allows a computer program to improve its own training
techniques to learn
complex patterns in large amounts of data.
When the computer program is given data,
it decides for itself
what features are relevant. There are many applications of AI
in the field of healthcare and medicine. AI has
been successful in the tracking of several diseases including covid-19
zika,
virus and Ebola. AI can do this by using machine learning to scour the internet, news sources, flight
mapping data, animal data,
social media platforms,genome databases
and various other Health Data to track and
predict the progression of these diseases and viruses.
For instance, companies can look at data from Health forums where
individuals might discuss an unusual onset of symptoms. From this,
AI can track when, where, and how these diseases are progressing.
AI was
able to accurately estimate the H1N1 outbreak through tracking
specific keywords on Twitter alone.
This type of data is structured which means
it is really difficult for computers to decode.
this data.
Structured data is more quantitative in nature and includes numbers and
reported cases,
which are easier to track.
AI today uses more complex,
algorithms that involves deep learning to be able to look at this unstructured data.
AI companies today use natural
language processing to
track data in different languages.
But for these models,
researchers must train using data for the algorithm to be
able to run on its own. Therefore,
the effectiveness of the AI to be able to accurately track and
predict disease,
progression and outbreaks depends on the quality and the quantity of
data. There are many examples of companies
that focus on disease
tracking. Some examples include HealthMap which uses social media, and news articles, Metabiota
which uses combines biological, political socio-economic
and environmental data to predict disease outbreaks, and BlueDot which tracks flight data.
In this video we will focus on BlueDot due to its relevance as a Canadian based company that has gained a lot of popularity since the COVID Pandemic.
since the covid-19 pandemic.
The toronto-based digital Health company was founded by an infectious
disease physician named Dr. Kamran Khan in 2013.
can detect 150 different pathogens from data sources that include
news reports, medical bulletins, climate data, livestock reports and human
population data
Data searches are performed every 15 minutes,
24 hours a day. When irregularities are detected during
the searches, the algorithm then tracks airline ticket sales from over 4,000,
airports across the world in order to predict how the
disease may progress.
At this time the computer alerts the user and then epidemiologist and
public health workers
look at the data to verify the results. Using this method, BlueDot was able to detect
the presence of unusual pneumonia cases in Wuhan, 9 days prior to being detected by the world health organization,
which was later known as covid-19.
The software was further able to predict 12 out of 20 of the
next cities to get hit with COVID cases based on airline ticket details.
Covid-19 was not the only time that BlueDot
has been proven as a successful tracking tool.
The outbreak of the zika virus was
also predicted by BlueDot.
The future implications of BludDot are vast when it comes to handling infectious disease outbreaks. Using Blue dot, scientists can detect outbreaks as early as possible
understand how diseases are dispersed to different parts of the world,
determine the consequences of the spread of diseases,
help protect populations and empower public health and
coordinate
emergency response.
It is apparent that BlueDot offers numerous benefits.
However,
it is important to address the limitations as well.
These include the ethical issues associated with accessing personal data.
Another limitation occurs
when biased or inaccurate data is used which could affect
results.
And,
lastly,
inaccurate predictions may lead to mass hysteria if released
to the public.
Despite the limitations, BlueDot and other AI companies will play an
integral role in future epidemic.
And pandemic tracking
which will help save countless lives.