Fwd: MSR Thesis Talk: Learning Cyber-Physical Models of Resuscitation

[Artur Dubrawski]

Team,

Mayra will be presenting her MSR work on Wednesday at noon.
Please join if available and if you are interested in cyber-physical models
and time series in critical care.

Cheers
Artur

---------- Forwarded message ---------
From: Mayra Melendez <mmelende at andrew.cmu.edu>
Date: Wed, Dec 2, 2020 at 4:28 PM
Subject: MSR Thesis Talk: Learning Cyber-Physical Models of Resuscitation
To: <ri-people at cs.cmu.edu>


Hello everyone,

I will be giving my MSR thesis talk on Wednesday, December 9th at 12:00 PM
EST via zoom.

*Date:* Wednesday, December 9th, 2020
*Time:* 12:00 PM - 1:00 PM EST
*Zoom link:*
https://cmu.zoom.us/j/2264817054?pwd=Nzlzb0lrZjVVZkhKZEhvTFlraXhXQT09
<https://www.google.com/url?q=https://cmu.zoom.us/j/2264817054?pwd%3DNzlzb0lrZjVVZkhKZEhvTFlraXhXQT09&sa=D&source=calendar&ust=1607376040681000&usg=AOvVaw3XMCi4SfNWvQ-K_6EA8p-5>
*Meeting ID:* 226 481 7054
*Password:* 268411

*Title:* Learning Cyber-Physical Models of Resuscitation

*Abstract: *
Ability to predict outcomes and forecast trajectories of recovery from
resuscitated intensive care patients could guide treatment decisions and
improve outcomes of care in both clinical and field settings. We develop a
machine learning driven cyber-physical model to provide such predictive
capabilities by leveraging arterial blood pressure (ABP) waveforms, one of
the routinely collected vital signs. A cohort of 51 Yorkshire pigs was
subjected to induced slow rate hemorrhage followed by fluid resuscitation.
To represent physics of the arterial system and emulate blood pressure
dynamics, we combine a two-element Windkessel model with an Unscented
Kalman Filter (UKF) to track the instantaneously estimated Windkessel
parameters over time. As the arterial pressure waveform exponentially
decays during diastole after each pump, we use UKF-tracked Windkessel
parameter estimates to identify time windows of ABP waveforms taken from
other subjects in the cohort to reconstruct the shapes of the test
subject’s ABP signal and its moving average. We allow UKF covariance to
temporarily increase to account for the effects of treatment such as
administering norepinephrine. When evaluated under leave-one-subject-out
cross-validation protocol, the model stays within 14+/-5% (mean+/-standard
deviation) of mean absolute percentage error when reconstructing the
current 250Hz ABP waveforms, and 19+/-6%, 24+/-6%, and 25+/-6% when
forecasting at 5, 15 and 30 minute horizons, respectively. Our results
demonstrate feasibility of using cyber-physical modeling of hemodynamic
waveform data to predict trajectories of resuscitation and therefore timely
inform treatment of hemorrhagic patients in both clinical and prolonged
field care settings.

*Committee:*
Artur Dubrawski (advisor)
Jeff Schneider
Nicholas Gisolfi

--
Mayra Melendez
MSR student, Robotics Institute - CMU
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