Fw: Second Paper Presentation - Maria DeArteaga - Friday, September 29 at 10:30 - Room 1204

[Maria De Arteaga Gonzalez]

Hi Autonians,

?

On Friday I will be presenting my qualifier work at Heinz College, you are all invited to attend. Best,


Maria


María De Arteaga
PhD Student in Machine Learning and Public Policy
Carnegie Mellon University
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From: Heinz-phd <heinz-phd-bounces+mdeartea=andrew.cmu.edu at lists.andrew.cmu.edu> on behalf of Michelle Wirtz <mwirtz at andrew.cmu.edu>
Sent: Friday, September 22, 2017 3:49 PM
To: heinz-faculty at lists.andrew.cmu.edu; Heinz-phd at lists.andrew.cmu.edu; clermontg at upmc.edu
Subject: Second Paper Presentation - Maria DeArteaga - Friday, September 29 at 10:30 - Room 1204

All,
Please join us Friday, September 29, 2017 in Hamburg Hall Room 1204 at 10:30 when Maria DeArteaga will be presenting her second paper.
Date and time: Friday, September 29, 10:30 in Hamburg Hall 1204.

Committee:  Artur Dubrawski (Chair), Gilles Clermont (UPMC), Alexandra Chouldechova

Title: Predicting Neurological Recovery with Canonical Autocorrelation Embeddings

Abstract:

In this work we present Canonical Autocorrelation Embeddings, a method for embedding sets of data points onto a space in which they are characterized in terms of their latent complex correlation structures, and where a distance metric enables the comparison of such structures. This methodology is particularly fitting to tasks where each individual or object of study has a batch of data points associated to it, as in for instance patients for whom several vital signs or other health related parameters are recorded over time.

We apply this new methodology to characterize patterns of brain activity of comatose survivors of cardiac arrest, aiming to predict whether they would have a positive neurological recovery. Clinicians routinely face the ethically and emotionally charged decision of whether to continue life support for such patients or not. Both scenarios have potentially grave implications on patients and their close ones, so regardless of whether they believe they have enough information, clinicians are often forced to make a prediction. Our results show that we can identify with high confidence a substantial number of patients who are likely to have a good neurological outcome. Providing this information to support clinical decisions could motivate the continuation of life-sustaining therapies for patients whose data suggest it to be the right choice.

Paper:  Attached
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