Researchers use artificial intelligence to unlock extreme weather
mysteries

Date:
August 10, 2021
Source:
Stanford University
Summary:
A new machine learning approach helps scientists understand
why extreme precipitation days in the Midwest are becoming more
frequent. It could also help scientists better predict how these
and other extreme weather events will change in the future.



FULL STORY ==========================================================================
From lake-draining drought in California to bridge-breaking floods
in China, extreme weather is wreaking havoc. Preparing for weather
extremes in a changing climate remains a challenge, however, because
their causes are complex and their response to global warming is often
not well understood. Now, Stanford researchers have developed a machine learning tool to identify conditions for extreme precipitation events
in the Midwest, which account for over half of all major U.S. flood
disasters. Published in Geophysical Research Letters,their approach
is one of the first examples using AI to analyze causes of long-term
changes in extreme events and could help make projections of such events
more accurate.


==========================================================================
"We know that flooding has been getting worse," said study lead author
Frances Davenport, a PhD student in Earth system science in Stanford's
School of Earth, Energy & Environmental Sciences (Stanford Earth). "Our
goal was to understand why extreme precipitation is increasing, which in
turn could lead to better predictions about future flooding." Among other impacts, global warming is expected to drive heavier rain and snowfall
by creating a warmer atmosphere that can hold more moisture.

Scientists hypothesize that climate change may affect precipitation in
other ways, too, such as changing when and where storms occur. Revealing
these impacts has remained difficult, however, in part because global
climate models do not necessarily have the spatial resolution to model
these regional extreme events.

"This new approach to leveraging machine learning techniques is opening
new avenues in our understanding of the underlying causes of changing extremes," said study co-author Noah Diffenbaugh, the Kara J Foundation Professor in the School of Earth, Energy & Environmental Sciences. "That
could enable communities and decision makers to better prepare for
high-impact events, such as those that are so extreme that they fall
outside of our historical experience." Davenport and Diffenbaugh
focused on the upper Mississippi watershed and the eastern part of
the Missouri watershed. The highly flood-prone region, which spans
parts of nine states, has seen extreme precipitation days and major
floods become more frequent in recent decades. The researchers started
by using publicly available climate data to calculate the number of
extreme precipitation days in the region from 1981 to 2019. Then they
trained a machine learning algorithm designed for analyzing grid data,
such as images, to identify large-scale atmospheric circulation patterns associated with extreme precipitation (above the 95th percentile).

"The algorithm we use correctly identifies over 90 percent of the extreme precipitation days, which is higher than the performance of traditional statistical methods that we tested," Davenport said.

The trained machine learning algorithm revealed that multiple
factors are responsible for the recent increase in Midwest extreme precipitation. During the 21st century, the atmospheric pressure patterns
that lead to extreme Midwest precipitation have become more frequent, increasing at a rate of about one additional day per year, although the researchers note that the changes are much weaker going back further in
time to the 1980s.

However, the researchers found that when these atmospheric pressure
patterns do occur, the amount of precipitation that results has clearly increased. As a result, days with these conditions are more likely to
have extreme precipitation now than they did in the past. Davenport and Diffenbaugh also found that increases in the precipitation intensity on
these days were associated with higher atmospheric moisture flows from
the Gulf of Mexico into the Midwest, bringing the water necessary for
heavy rainfall in the region.

The researchers hope to extend their approach to look at how these
different factors will affect extreme precipitation in the future. They
also envision redeploying the tool to focus on other regions and types
of extreme events, and to analyze distinct extreme precipitation causes,
such as weather fronts or tropical cyclones. These applications will
help further parse climate change's connections to extreme weather.

"While we focused on the Midwest initially, our approach can
be applied to other regions and used to understand changes
in extreme events more broadly," said Davenport. "This will
help society better prepare for the impacts of climate change." ========================================================================== Story Source: Materials provided by Stanford_University. Original written
by Rob Jordan.

Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Frances V. Davenport, Noah S. Diffenbaugh. Using Machine Learning to
Analyze Physical Causes of Climate Change: A Case Study of
U.S. Midwest Extreme Precipitation. Geophysical Research Letters,
2021; 48 (15) DOI: 10.1029/2021GL093787 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2021/08/210810143117.htm

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Researchers use artificial intelligence to predict which COVID-19
patients will need a ventilator to breathe

Date:
September 2, 2021
Source:
Case Western Reserve University
Summary:
Researchers have developed an online tool to help medical staff
quickly determine which COVID-19 patients will need help breathing
with a ventilator. The tool, developed through analysis of CT
scans from nearly 900 COVID-19 patients diagnosed in 2020, was
able to predict ventilator need with 84 percent accuracy.



FULL STORY ========================================================================== Researchers at Case Western Reserve University have developed an online
tool to help medical staff quickly determine which COVID-19 patients
will need help breathing with a ventilator.


==========================================================================
The tool, developed through analysis of CT scans from nearly 900 COVID-19 patients diagnosed in 2020, was able to predict ventilator need with
84% accuracy.

"That could be important for physicians as they plan how to care for a
patient -- and, of course, for the patient and their family to know,"
said Anant Madabhushi, the Donnell Institute Professor of Biomedical Engineering at Case Western Reserve and head of the Center for
Computational Imaging and Personalized Diagnostics (CCIPD). "It could
also be important for hospitals as they determine how many ventilators
they'll need." Next, Madabhushi said he hopes to use those results to
try out the computational tool in real time at University Hospitals and
Louis Stokes Cleveland VA Medical Center with COVID-19 patients.

If successful, he said medical staff at the two hospitals could upload
a digitized image of the chest scan to a cloud-based application, where
the AI at Case Western Reserve would analyze it and predict whether that patient would likely need a ventilator.

Dire need for ventilators Among the more common symptoms of severe
COVID-19 cases is the need for patients to be placed on ventilators
to ensure they will be able to continue to take in enough oxygen as
they breathe.



==========================================================================
Yet, almost from the start of the pandemic, the number of ventilators
needed to support such patients far outpaced available supplies -- to
the point that hospitals began "splitting" ventilators -- a practice in
which a ventilator assists more than one patient.

While 2021's climbing vaccination rates dramatically reduced COVID-19 hospitalization rates -- and, in turn, the need for ventilators -- the
recent emergence of the Delta variant has again led to shortages in some
areas of the United States and in other countries.

"These can be gut-wrenching decisions for hospitals -- deciding who is
going to get the most help against an aggressive disease," Madabhushi
said.

To date, physicians have lacked a consistent and reliable way to identify
which newly admitted COVID-19 patients are likely to need ventilators -- information that could prove invaluable to hospitals managing limited
supplies.

Researchers in Madabhushi's lab began their efforts to provide such
a tool by evaluating the initial scans taken in 2020 from nearly 900
patients from the U.S. and from Wuhan, China -- among the first known
cases of the disease caused by the novel coronavirus.



========================================================================== Madabhushi said those CT scans revealed -- with the help of deep-learning computers, or Artificial Intelligence (AI) -- distinctive features for
patients who later ended up in the intensive care unit (ICU) and needed
help breathing.

The research behind the tool appeared this month in the IEEE Journal of Biomedical and Health Informatics.

Amogh Hiremath, a graduate student in Madabhushi's lab and lead author
on the paper, said patterns on the CT scans couldn't be seen by the
naked eye, but were revealed only by the computers.

"This tool would allow for medical workers to administer medications
or supportive interventions sooner to slow down disease progression,"
Hiremath said. "And it would allow for early identification of those at increased risk of developing severe acute respiratory distress syndrome
-- or death. These are the patients who are ideal ventilator candidates." Further research into 'immune architecture' Madabhushi's lab also recently published research comparing autopsy tissues scans taken from patients
who died from the H1N1 virus (Swine Flu) and from COVID-19. While the
results are preliminary, they do appear to reveal information about
what Madabhushi called the "immune architecture" of the human body in
response to the viruses.

"This is important because the computer has given us information that
enriches our understanding of the mechanisms in the body against viruses,"
he said.

"That can play a role in how we develop vaccines, for example." Germa'n Corredor Prada, a research associate in Madabhushi's lab who was the
primary author on the paper, said computer vision and AI techniques
allowed the scientists to study how certain immune cells organize in
the lung tissue of some patients.

"This allowed us to find information that may not be obvious by simple
visual inspection of the samples," Corredor said. "These COVID-19-related patterns seem to be different from those of other diseases such as H1N1, a comparable viral disease." Eventually, when combined with other clinical
work and further tests in larger sets of patients, this discovery could
serve to improve the world's understanding of these diseases and maybe
others, he said.

Madabhushi established the CCIPD at Case Western Reserve in 2012. The
lab now includes more than 60 researchers. Some were involved in this
most recent COVID-19 work, including graduate students Hiremath, Pranjal Vaidya; research associates Corredor and Paula Toro; and research faculty
Cheng Lu and Mehdi Alilou.

========================================================================== Story Source: Materials provided by Case_Western_Reserve_University. Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Amogh Hiremath, Kaustav Bera, Lei Yuan, Pranjal Vaidya, Mehdi
Alilou,
Jennifer Furin, Keith Armitage, Robert Gilkeson, Mengyao Ji, Pingfu
Fu, Amit Gupta, Cheng Lu, Anant Madabushi. Integrated Clinical and
CT based Artificial Intelligence nomogram for predicting severity
and need for ventilator support in COVID-19 patients: A multi-site
study. IEEE Journal of Biomedical and Health Informatics, Aug. 13,
2021; DOI: 10.1109/ JBHI.2021.3103389 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2021/09/210902174814.htm

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