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<div style="margin: 0in 0in 0.0001pt;" class=""><font face="Arial" class="">Our next Mobilize Center Seminar is scheduled for Thursday, April 6th, and features <b class="">Ina Fiterau</b>,<b class=""> </b>one of our<b class=""> </b>Mobilize Center Distinguished
postdoctoral fellows. She will be presenting “Leveraging Activity Data for the Prediction of Clinical Outcomes.”<o:p class=""></o:p></font></div>
<div style="margin: 0in 0in 0.0001pt;" class=""><font face="Arial" class=""> </font></div>
<div style="margin: 0in 0in 0.0001pt;" class=""><font face="Arial" class="">Please also note that starting in April, our seminars will be held on a monthly basis (see Spring quarter schedule below). We look forward to seeing you next week and at future seminars!<o:p class=""></o:p></font></div>
<div style="margin: 0in 0in 0.0001pt;" class=""><font face="Arial" class=""> <o:p class=""></o:p></font></div>
<div style="margin: 0in 0in 0.0001pt;" class=""><font face="Arial" class=""><b class="">TITLE:</b> <o:p class=""></o:p></font></div>
<div style="margin: 0in 0in 0.0001pt;" class=""><font face="Arial" class=""><b class="">Leveraging Activity Data for the Prediction of Clinical Outcomes </b><o:p class=""></o:p></font></div>
<div style="margin: 0in 0in 0.0001pt;" class=""><font face="Arial" class=""> <o:p class=""></o:p></font></div>
<div style="margin: 0in 0in 0.0001pt;" class=""><font face="Arial" class=""><b class="">WHEN:</b> <o:p class=""></o:p></font></div>
<div style="margin: 0in 0in 0.0001pt;" class=""><font face="Arial" class=""><b class="">Thursday, April 6</b><o:p class=""></o:p></font></div>
<div style="margin: 0in 0in 0.0001pt;" class=""><font face="Arial" class=""><b class="">noon - 1 pm</b> <o:p class=""></o:p></font></div>
<div style="margin: 0in 0in 0.0001pt;" class=""><font face="Arial" class=""> <o:p class=""></o:p></font></div>
<div style="margin: 0in 0in 0.0001pt;" class=""><font face="Arial" class=""><b class="">WHERE:</b> <o:p class=""></o:p></font></div>
<div style="margin: 0in 0in 0.0001pt;" class=""><font face="Arial" class=""><b class=""><span style="color: rgb(251, 2, 7);" class="">Y2E2 300, Stanford University </span></b><o:p class=""></o:p></font></div>
<div style="margin: 0in 0in 0.0001pt;" class=""><font face="Arial" class=""> <o:p class=""></o:p></font></div>
<div style="margin: 0in 0in 0.0001pt;" class=""><font face="Arial" class=""><b class="">Abstract:</b><o:p class=""></o:p></font></div>
<div style="margin: 0in 0in 0.0001pt;" class=""><font face="Arial" class="">Wrist and hip-worn accelerometers have become standard tools for monitoring movement over extended time periods. They offer the possibility to determine long-term activity patterns that
are instrumental for understanding obesity causes, prevention and control. Meaningful analysis of the real world data depends on the identification of intervals when the device is not worn. Lack of true knowledge of non-wear status, together with its similarity
with sedentary behavior and sleep, makes this task difficult. <o:p class=""></o:p></font></div>
<div style="margin: 0in 0in 0.0001pt;" class=""><o:p class=""><font face="Arial" class=""> </font></o:p></div>
<div style="margin: 0in 0in 0.0001pt;" class=""><font face="Arial" class="">First, I will present an ensemble method for non-wear detection that leverages, in an unsupervised way, the predictions of existing wear/non-wear/sleep classification methods. Since
field data are inevitably different from the data on which the predictors were trained or evaluated, their true accuracy is unknown. We derive accuracy estimators, which we obtain based on experts’ consistency of predictions by solving a linear system. We
then use the accuracy estimates in a weighting scheme for the ensemble classification, in order to obtain superior predictions compared to the original classifiers.<o:p class=""></o:p></font></div>
<div style="margin: 0in 0in 0.0001pt;" class=""><o:p class=""><font face="Arial" class=""> </font></o:p></div>
<div style="margin: 0in 0in 0.0001pt;" class=""><font face="Arial" class="">In the second part of the talk I will describe ShortFuse, a deep learning method which builds representations from temporal features while incorporating the structured information into
the deep learning models applied to the temporal features. We show that the inclusion of the structured covariates results in improved performance over the state-of-the-art for biomedical classification tasks. We also study the impact that individual covariates
and sequences have on the performance of the learned representations, facilitating expert interpretation.<o:p class=""></o:p></font></div>
<div style="margin: 0in 0in 0.0001pt; text-align: justify;" class=""><font face="Arial" class=""> </font></div>
<div style="margin: 0in 0in 0.0001pt;" class=""><font face="Arial" class=""><b class="">Upcoming Mobilize Center seminars:</b><font color="#ff40ff" class=""><o:p class=""></o:p></font></font></div>
<div style="margin: 0in 0in 0.0001pt;" class=""><span style="color: rgb(255, 64, 255);" class=""><font face="Arial" class=""> </font></span></div>
<div style="margin: 0in 0in 0.0001pt;" class=""><font face="Arial" class="">May 4 -- <o:p class=""></o:p><a href="http://www2.stat.duke.edu/~kheller/" class="">Katherine Heller, Duke University</a></font></div>
<div style="margin: 0in 0in 0.0001pt;" class=""><font face="Arial" class="">June 1 – <o:p class=""></o:p><a href="https://engineering.stanford.edu/people/peter-bailis" class="">Peter Bailis, Stanford University</a></font></div>
<div style="margin: 0in 0in 0.0001pt;" class=""><font face="Arial" class="">June 8 – <a href="https://lsa.umich.edu/stats/people/faculty/tewaria.html" class="">Ambuj Tewari, University of Michigan</a></font></div>
<div style="margin: 0in 0in 0.0001pt;" class=""><o:p class=""><font face="Arial" class=""> </font></o:p></div>
<div style="margin: 0in 0in 0.0001pt;" class=""><font face="Arial" class="">To keep up-to-date on upcoming speakers and the dates, visit </font><a href="http://mobilize.stanford.edu/events/" class="">Mobilize Events</a><font face="Arial" class="">.<o:p class=""></o:p></font></div>
<div style="margin: 0in 0in 0.0001pt;" class=""><span style="color: rgb(31, 73, 125);" class=""><font face="Arial" class=""> </font></span></div>
<div style="margin: 0in 0in 0.0001pt;" class=""><span style="color: rgb(31, 73, 125);" class=""><font face="Arial" class=""> </font></span></div>
<div class="">
<div class=""><font face="Arial" class="">Diane Bush<br class="">
Assistant to Professor Scott Delp<br class="">
NMBL, Mobilize Center, OpenSim<br class="">
Stanford University<br class="">
<a href="mailto:dbush1@stanford.edu" class="">dbush1@stanford.edu</a><br class="">
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