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<div><span style="color: rgb(33, 33, 33); font-family: Roboto, Helvetica, Arial, sans-serif; font-size: 14px; background-color: rgb(255, 255, 255); display: inline !important;">This is an announcement of Chao-Chun Hsu's MS Presentation</span><br style="box-sizing: content-box; color: rgb(33, 33, 33); font-family: Roboto, Helvetica, Arial, sans-serif; font-size: 14px;">
<span style="color: rgb(33, 33, 33); font-family: Roboto, Helvetica, Arial, sans-serif; font-size: 14px; background-color: rgb(255, 255, 255); display: inline !important;">===============================================</span><br style="box-sizing: content-box; color: rgb(33, 33, 33); font-family: Roboto, Helvetica, Arial, sans-serif; font-size: 14px;">
<span style="color: rgb(33, 33, 33); font-family: Roboto, Helvetica, Arial, sans-serif; font-size: 14px; background-color: rgb(255, 255, 255); display: inline !important;">Candidate: Chao-Chun Hsu</span><br style="box-sizing: content-box; color: rgb(33, 33, 33); font-family: Roboto, Helvetica, Arial, sans-serif; font-size: 14px;">
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<span style="color: rgb(33, 33, 33); font-family: Roboto, Helvetica, Arial, sans-serif; font-size: 14px; background-color: rgb(255, 255, 255); display: inline !important;">Date: Wednesday, March 01, 2023</span><br style="box-sizing: content-box; color: rgb(33, 33, 33); font-family: Roboto, Helvetica, Arial, sans-serif; font-size: 14px;">
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<span style="color: rgb(33, 33, 33); font-family: Roboto, Helvetica, Arial, sans-serif; font-size: 14px; background-color: rgb(255, 255, 255); display: inline !important;">Time: 3 pm CST</span><br style="box-sizing: content-box; color: rgb(33, 33, 33); font-family: Roboto, Helvetica, Arial, sans-serif; font-size: 14px;">
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<span style="color: rgb(33, 33, 33); font-family: Roboto, Helvetica, Arial, sans-serif; font-size: 14px; background-color: rgb(255, 255, 255); display: inline !important;">Location: JCL 298</span>
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Remote Location: <a href="https://www.google.com/url?q=https://uchicago.zoom.us/j/94132202998?pwd%3DTFRpUXJrUW1KVW9udTJ2Zy81OVJ2UT09&sa=D&source=calendar&ust=1678026497926401&usg=AOvVaw1pxA7LKZ_mNapnGc7nK396" target="_blank" rel="noopener noreferrer" data-auth="NotApplicable" style="box-sizing: content-box; margin: 0px; orphans: 2; widows: 2;"><u style="box-sizing:content-box">https://uchicago.zoom.us/j/94132202998?pwd=TFRpUXJrUW1KVW9udTJ2Zy81OVJ2UT09</u></a><span style="box-sizing: content-box; margin: 0px; color: rgb(60, 64, 67); orphans: 2; widows: 2;"> (ID:
94132202998, passcode: H2965Wmi)</span><br style="box-sizing:content-box">
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M.S. Paper Title: On Predictability of Physician Notes<br style="box-sizing:content-box">
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Abstract: The success of large language models not only leads to substantial progress in NLP systems, but also provides exciting opportunities to characterize the predictability of human texts.<br style="box-sizing:content-box">
In this work, we conduct the first large-scale systematic study of predictability in a wide range of text corpora, with an emphasis on physician notes.<br style="box-sizing:content-box">
We show that physician notes are much more predictable than other texts.<br style="box-sizing:content-box">
For example, the perplexity is only about 3 on physician notes, compared to 19 on Wikipedia and 12 on PubMed abstracts.<br style="box-sizing:content-box">
We further examine the predictability of different physicians and patients, characterize what information is hard to predict, and investigate the flow of predictability in texts.<br style="box-sizing:content-box">
Results show that notes for females and minorities groups have significantly lower perplexity compared to males and whites respectively.<br style="box-sizing:content-box">
Perplexity flow analysis further reveals which sections in the physician note drive the differences between groups.<br style="box-sizing:content-box">
Last but not least, we show that perplexity can serve as useful features for predicting clinical outcomes.<br style="box-sizing:content-box">
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Advisors: Chenhao Tan<br style="box-sizing:content-box">
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Committee Members: Chenhao Tan, Allyson Ettinger, and Ziad Obermeyer</div>
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