<?xml version="1.0" encoding="UTF-8"?><oembed><type>video</type><version>1.0</version><html>&lt;iframe src=&quot;https://www.loom.com/embed/806355dc51964581a168c5aeb4fd4921&quot; frameborder=&quot;0&quot; width=&quot;1920&quot; height=&quot;1440&quot; webkitallowfullscreen mozallowfullscreen allowfullscreen&gt;&lt;/iframe&gt;</html><height>1440</height><width>1920</width><provider_name>Loom</provider_name><provider_url>https://www.loom.com</provider_url><thumbnail_height>1440</thumbnail_height><thumbnail_width>1920</thumbnail_width><thumbnail_url>https://cdn.loom.com/sessions/thumbnails/806355dc51964581a168c5aeb4fd4921-5ba8bbaefa9b1108.gif</thumbnail_url><duration>300.453</duration><title>Sevesing at AI: Predicting Sepsis 🤖</title><description>In this video, I present my project titled &quot;Sevesing at AI,&quot; which is a predictive application designed to identify patients at risk of sepsis using data from the milledRx application. The model boasts an impressive accuracy of 99.8%, showcasing its effectiveness in early detection. I walk through the application&apos;s functionalities, including patient data management and real-time monitoring. Please take a look at the features and let me know your thoughts!</description></oembed>