C3.ai DTI's Digital Transformation Science Colloquium Series features free weekly talks from researchers who work to mitigate COVID-19. Join us on Zoom Thursday, February 4 at 3:00 p.m. U.S. Central time and hear from Illinois ECE Prof. Narendra Ahuja on "Triaging of COVID-19 Patients on Audio-Visual Cues." Registration for this event is required.
Abstract: The COVID-19 pandemic has placed unprecedented stress on hospital capacity. Increased emergency department (ED) patient volumes and admission rates have led to a scarcity in beds. Bed-sparing protocols that identify COVID-19 patients stable for discharge from the ED or early hospital discharge have proven elusive given this population's propensity to rapidly deteriorate up to one week after illness onset. Consequently, a significant number of stable patients are unnecessarily admitted to the hospital while some discharged patients decompensate at home and subsequently require emergency transport to the ED. In order to conserve hospital beds, there is an urgent need for improved methods for assessing clinical stability of COVID-19 patients. In this talk, we will describe our project's immediate goal to develop audiovisual tools to reproduce common physical exam findings. These will be subsequently used to predict clinical decompensation from patient videos captured using consumer grade smartphones. These tools will be tested on COVID-19 and other pulmonary patient populations. We will start collecting patient data at UIC and UC hospitals in January 2021 and are developing explainable artificial intelligence and machine learning algorithms for predicting impending deterioration from health-relevant audiovisual features and provide explanations in terms of the clinical details within the electronic health record. Once validated on our patient data, the tools will provide clinical assessments of COVID-19 patients both at the bedside and across telemedicine platforms during virtual follow-ups. The techniques and algorithms developed in this project are likely to be applicable to other high-risk patient populations and emerging platforms, such as telemedicine.
