{"type":"video","version":"1.0","html":"<iframe src=\"https://www.loom.com/embed/806355dc51964581a168c5aeb4fd4921\" frameborder=\"0\" width=\"1920\" height=\"1440\" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>","height":1440,"width":1920,"provider_name":"Loom","provider_url":"https://www.loom.com","thumbnail_height":1440,"thumbnail_width":1920,"thumbnail_url":"https://cdn.loom.com/sessions/thumbnails/806355dc51964581a168c5aeb4fd4921-5ba8bbaefa9b1108.gif","duration":300.453,"title":"Sevesing at AI: Predicting Sepsis 🤖","description":"In this video, I present my project titled \"Sevesing at AI,\" 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's functionalities, including patient data management and real-time monitoring. Please take a look at the features and let me know your thoughts!"}