“Uber, the world’s largest taxi
company, owns no vehicles. Facebook, the world’s most popular media owner,
creates no content. Alibaba, the most valuable retailer, has no inventory.
Netflix, the world’s largest movie house, own no cinemas. And Airbnb, the
world’s largest accommodation provider, owns no real estate. Something
interesting is happening.”
– Tom Goodwin, an executive at the French media group Havas.
This new breed of companies is the
fastest growing in history because they own the customer interface layer. It is
the platform where all the value and profit is. “Platform business” is a
more wholesome termfor this model for which data is the fuel; Big Data & AI/ML
technologies are the harbinger of new waves of productivity growth and
innovation.
With Big data and AI/ML is making a
big difference in the area of public health, let’s see how it is helping us
tackle the global emergency of coronavirus formally known as COVID-19.
“With rapidly spreading disease, a
two-week lag is an eternity.”
DISCOVERING/ DETECTING
Chinese
technology giant Alibaba has developed an AI system for detecting the COVID-19
in CT scans of patients’ chests with 96% accuracy against viral
pneumonia cases. It only takes 20 seconds for the AI to decide, whereas humans
generally take about 15 minutes to diagnose the illness as there can be upwards
of 300 images to evaluate.The system was trained on images and data from
5,000 confirmed coronavirus cases and has been tested in hospitals throughout
China. Per a report, at least 100 healthcare facilities are currently employing
Alibaba’s AI to detect COVID-19.
Ping An Insurance (Group) Company of China, Ltd (Ping An) aims to
address the issue of lack of radiologists by introducing the COVID-19 smart
image-reading system. This image-reading system can read the huge volumes of CT
scans in epidemic areas.
Ping
An Smart Healthcare uses clinical data to train the AI model of the COVID-19
smart image-reading system. The AI analysis engine conducts a comparative
analysis of multiple CT scan images of the same patient and measures the
changes in lesions. It helps in tracking the development of the disease,
evaluation of the treatment and in prognosis of patients.Ultimately it assists
doctors to diagnose, triage and evaluate COVID-19 patients swiftly and
effectively.
Ping
An Smart Healthcare’s COVID-19 smart image-reading system also supports AI
image-reading remotely by medical professionals outside the epidemic
areas.Since its launch, the smart image-reading system has provided services to
more than 1,500 medical institutions. More than 5,000 patients have received
smart image-reading services for free.
The
more solutions the better. At least when it comes to helping overwhelmed
doctors provide better diagnoses and, thus, better outcomes.
MONITORING
- AI
based Temperature monitoring & scanning
In
Beijing, China, subway passengers are being screened for symptoms of
coronavirus, but not by health authorities. Instead, artificial intelligence is
in-charge.
Two
Chinese AI giants, Megvii and Baidu, have introduced temperature-scanning. They
have implemented scanners to detect body temperature and send alerts to company
workers if a person’s body temperature is high enough to constitute a fever.
Megvii’s
AI system detects body temperatures for up to 15 people per second andup to 16
feet. It monitors as many as 16 checkpoints in a single station. The system
integrates body detection, face detection, and dual sensing via infrared
cameras and visible light. The system can accurately detect and flag high body
temperature even when people are wearing masks, hats, or covering their faces
with other items. Megvii’s system also sends alerts to an on-site staff member.
Baidu,
one of the largest search-engine companies in China, screens subway passengers
at the Qinghe station with infrared scanners. It also uses a facial-recognition
system, taking photographs of passengers’ faces. If the Baidu system detects a body
temperature of at least 99-degrees Fahrenheit, it sends an alert to the staff
member for another screening. The technology can scan the temperatures of more
than 200 people per minute.
- AI
based Social Media Monitoring
An international team is using machine learning to scour
through social media posts, news reports, data from official public health
channels, and information supplied by doctors for warning signs of the virus
across geographies.The program is looking for social media posts that mention
specific symptoms, like respiratory problems and fever, from a geographic area
where doctors have reported potential cases. Natural language processing is
used to parse the text posted on social media, for example, to distinguish
between someone discussing the news and someone complaining about how they
feel.
The approach has proven capable of spotting a coronavirus
needle in a haystack of big data. This technique could help experts learn how
the virus behaves. It may be possible to determine the age, gender, and location
of those most at risk quicker than using official medical sources.
PREDICTING
Data from hospitals, airports, and other public locations are
being used to predict disease spread and risk. Hospitals can also use the data
to plan for the impact of an outbreak on their operations.
Kalman Filter
Kalman filter was pioneered by Rudolf Emil Kalman in 1960,
originally designed and developed to solve the navigation problem in the Apollo
Project. Since then, it has been applied to numerous cases such as guidance,
navigation, and control of vehicles, computer vision’s object tracking,
trajectory optimization, time series analysis in signal processing,
econometrics and more.
Kalman filter is a recursive algorithm which uses time-series
measurement over time, containing statistical noise and produce estimations of
unknown variables.
For the one-day prediction Kalman filter can be used, while for
the long-term forecast a linear model is used where its main features are
Kalman predictors, infected rate relative to population, time-depended
features, and weather history and forecasting.
The one-day Kalman prediction is very accurate and powerful while
a longer period prediction is more challenging but provides a future trend.Long
term prediction does not guarantee full accuracy but provides a fair estimation
following the recent trend. The model should re-run daily to gain better
results.
GitHub Link: https://github.com/Rank23/COVID19
ANALYZING
The Center for Systems Science and
Engineering at Johns Hopkins University has developed an interactive, web-based
dashboard that tracks the status of COVID-19 around the world. The resource
provides a visualization of the location and number of confirmed COVID-19
cases, deaths and recoveries for all affected countries.
The primary data source for the
tool is DXY, a Chinese platform that aggregates local media and government
reports to provide COVID-19 cumulative case totals in near real-time at the
province level in China and country level otherwise. Additional data comes from
Twitter feeds, online news services and direct communication sent through the
dashboard. Johns Hopkins then confirms the case numbers with regional and local
health departments. This kind of Data analytics platform plays a pivotal role
in addressing the coronavirus outbreak.
All data from the dashboard is also
freely available in the following GitHub repository.
GitHub Link:https://bit.ly/2Wmmbp8
Mobile version: https://bit.ly/2WjyK4d
Web version: https://bit.ly/2xLyT6v
Conclusion
One of AI’s core strengths when
working on identifying and limiting the effects of virus outbreaks is its
incredibly insistent nature. AIsystems never tire, can sift through enormous
amounts of data, and identify
possible correlations and causations that humans can’t.
However, there are
limits to AI’s ability to both identify virus outbreaks and predict how
they will spread. Perhaps the best-known example comes from the neighboring
field of big data analytics. At its launch, Google Flu
Trends was heralded as a great leap forward in relation to
identifying and estimating the spread of the flu—until it underestimated the
2013 flu season by a whopping 140 percent and was quietly put to rest.Poor data
quality was identified as one of the main reasons Google Flu Trends
failed. Unreliable or faulty data can wreak havoc on the prediction
power of AI.
References:
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