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<p class="MsoNormal"><span style="font-size:13.5pt;font-family:"Arial",sans-serif;color:black"><img width="599" height="171" style="width:6.2395in;height:1.7812in" id="Picture_x0020_2" src="cid:image001.png@01DB30FE.E92034B0" alt="Dissertation Defense Announcement at the Cullen College of Engineering"></span><span style="font-size:13.5pt;font-family:"Arial",sans-serif;color:black;mso-ligatures:none"><o:p></o:p></span></p>
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<b><span style="font-size:18.0pt;font-family:"Times New Roman",serif;color:#C8102E;mso-ligatures:none">Predictive Analytics for Electronic Health Records Data Using Deep Learning</span></b><span style="font-size:18.0pt;font-family:"Times New Roman",serif;color:#C8102E;mso-ligatures:none"><o:p></o:p></span></p>
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<b><span style="font-size:13.5pt;font-family:"Times New Roman",serif;color:black;mso-ligatures:none">Abraham Bautista-Castillo</span></b><span style="font-size:13.5pt;font-family:"Times New Roman",serif;mso-ligatures:none"><o:p></o:p></span></p>
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<span style="font-size:10.5pt;font-family:"Arial",sans-serif;color:black;mso-ligatures:none">November 27, 2024; 11 a.m. – 1 p.m. (CST)<br>
Location: </span><span style="color:black;mso-ligatures:none"><a href="https://urldefense.com/v3/__https://teams.microsoft.com/l/meetup-join/19*3ameeting_Y2JhZWE2ZDAtZTViYi00MmRmLWI0NmItZDQ1YzYyZWExMWE0*40thread.v2/0?context=*7b*22Tid*22*3a*22170bbabd-a2f0-4c90-ad4b-0e8f0f0c4259*22*2c*22Oid*22*3a*226d9683f5-fa35-4de9-93b0-b37257beedd2*22*7d__;JSUlJSUlJSUlJSUlJSUl!!LkSTlj0I!Fu4JrKPYShFthPy_KquVuaPkGZqk0TKCf1DjVWyf0c1Alue--Gp65zurrlYd_kk5zvmcjwhcB5xuYEv8g1txBpy2XmQ$"><span style="font-size:10.5pt;font-family:"Arial",sans-serif;color:#0563C1">Virtual
Microsoft Teams</span></a></span><span style="font-size:10.5pt;font-family:"Arial",sans-serif;color:black;mso-ligatures:none">
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<b><span style="font-size:10.5pt;font-family:"Arial",sans-serif;color:black;mso-ligatures:none">Committee Chair:</span></b><span style="font-size:10.5pt;font-family:"Arial",sans-serif;color:black;mso-ligatures:none"><br>
Ioannis A Kakadiaris, Ph.D.</span><span style="font-size:10.5pt;font-family:"Arial",sans-serif;mso-ligatures:none"><o:p></o:p></span></p>
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<b><span style="font-size:10.5pt;font-family:"Arial",sans-serif;color:black;mso-ligatures:none">Committee Members:</span></b><span style="font-size:10.5pt;font-family:"Arial",sans-serif;color:black;mso-ligatures:none"><br>
Joseph T Francis, Ph.D. | Chandra Mohan, Ph.D. | Winston Liaw, Ph.D. | <br>
Tiphanie P Vogel, M.D., Ph.D.</span><span style="font-size:10.5pt;font-family:"Arial",sans-serif;mso-ligatures:none"><o:p></o:p></span></p>
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<b><span style="font-size:12.0pt;font-family:"Arial",sans-serif;color:#C8102E;mso-ligatures:none">Abstract</span></b><span style="font-size:12.0pt;font-family:"Arial",sans-serif;color:#C8102E;mso-ligatures:none"><o:p></o:p></span></p>
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<span style="font-size:10.5pt;font-family:"Arial",sans-serif;color:black;mso-ligatures:none">Predictive analytics using Electronic Health Records (EHR) has grown considerably in recent decades due to the emergence and adoption of new methods, such as Deep Learning,
to face several diagnostic challenges. During the COVID-19 pandemic, many of these diagnostic challenges have appeared, including the post-infectious sequelae multisystem inflammatory syndrome in children (MIS-C). This syndrome shares several clinical features
with other entities, such as Kawasaki disease (KD) and endemic typhus, among other febrile diseases. Endemic typhus, or murine typhus, is an acute infection treated much differently than MIS-C and KD. Early diagnosis and appropriate treatment are crucial to
a favorable outcome for patients with these disorders. To address these challenges, different AI-based algorithms using EHR can be implemented to support medical teams' decision-making to differentiate between these febrile conditions. This research proposes
two Clinical Decision Support Systems (CDSS), one based on a Two-Stage CDSS powered by an Attention-LSTM network (AI-MET) and another based on a Triplet Loss Siamese network (AI-HEAT), to distinguish between patients presenting with clinically similar pediatric
febrile conditions using clinical and laboratory features typically available within six hours of presentation. The study utilizes a comprehensive dataset of pediatric patients diagnosed with MIS-C, KD, or endemic typhus, collected from multiple healthcare
institutions across the United States. The findings presented in this work are strong evidence that the two robust AI-based Clinical Decision Support Systems proposed can accurately differentiate between MIS-C, KD, and endemic typhus using early clinical and
laboratory data available during the first six hours after the patient's arrival. These tools have the potential to significantly improve the diagnostic process for clinically similar pediatric febrile conditions, ultimately leading to more timely and appropriate
treatment decisions.</span><span style="font-size:10.5pt;font-family:"Arial",sans-serif;mso-ligatures:none"><o:p></o:p></span></p>
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<p class="MsoNormal"><span style="font-size:13.5pt;font-family:"Arial",sans-serif;color:black"><img border="0" width="599" height="82" style="width:6.2395in;height:.8541in" id="Picture_x0020_1" src="cid:image002.png@01DB30FE.E92034B0" alt="Engineered For What's Next"></span><span style="font-size:13.5pt;font-family:"Arial",sans-serif;color:black;mso-ligatures:none"><o:p></o:p></span></p>
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