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<p>Hi Autonians,<br>
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<p>On Friday I will be presenting my qualifier work at Heinz College, you are all invited to attend. Best,<br>
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<p>Maria<br>
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<div style="font-family:Tahoma; font-size:13px">Mar<span style="line-height:18px; background-color:rgb(254,251,243)">ķa De Arteaga </span>
<div>PhD Student in Machine Learning and Public Policy</div>
<div>Carnegie Mellon University</div>
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<div id="divRplyFwdMsg" dir="ltr"><font face="Calibri, sans-serif" color="#000000" style="font-size:11pt"><b>From:</b> Heinz-phd <heinz-phd-bounces+mdeartea=andrew.cmu.edu@lists.andrew.cmu.edu> on behalf of Michelle Wirtz <mwirtz@andrew.cmu.edu><br>
<b>Sent:</b> Friday, September 22, 2017 3:49 PM<br>
<b>To:</b> heinz-faculty@lists.andrew.cmu.edu; Heinz-phd@lists.andrew.cmu.edu; clermontg@upmc.edu<br>
<b>Subject:</b> Second Paper Presentation - Maria DeArteaga - Friday, September 29 at 10:30 - Room 1204</font>
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<p class="MsoNormal">All,</p>
<p class="MsoNormal" style="">Please join us<span style="color:#1F497D"> </span>Friday, September 29, 2017 in Hamburg Hall Room 1204 at 10:30 when Maria DeArteaga will be presenting her second paper. </p>
<p class="MsoNormal"><b>Date and time:</b> Friday, September 29, 10:30 in Hamburg Hall 1204.
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<p class="MsoNormal"><b>Committee: </b><span style="color:black">Artur Dubrawski (Chair), Gilles Clermont (UPMC), Alexandra Chouldechova</span><b><span style="color:#1F497D"></span></b></p>
<p class="MsoNormal"><span style="font-size:11.0pt; font-family:"Calibri","sans-serif"; color:#1F497D"> </span></p>
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<p class="MsoNormal"><b>Title: </b><em><span style="color:black; font-style:normal">Predicting Neurological Recovery with Canonical Autocorrelation Embeddings</span></em><i></i></p>
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<p class="MsoNormal"><b>Abstract:</b></p>
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<p><span style="color:black">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. </span></p>
<p><span style="color:black">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.
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<p class="MsoNormal"><b>Paper:<span style="color:#1F497D"> </span></b>Attached</p>
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