SBIR Phase I Machine Learning for Screening Acute Respiratory Distress Syndrome in General and COVID-19 Patient Populations

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The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project will be to improve early and accurate acute respiratory distress syndrome (ARDS) detection. ARDS detection is vital due to the recent COVID-19 outbreak and the propensity for individuals testing positive for COVID-19 to develop ARDS as a serious complication, as well as the 140,000 patients per year in the United States admitted with ARDS. The ARDS diagnostic market in the US was an estimated $154 million in 2018. This project will advance a machine-learning algorithm to accurately predict ARDS onset in the COVID-19 patient population. These systems will monitor patient electronic health records and automatically provide ARDS prediction alerts for both general and COVID-19 patient populations, thereby enabling appropriate intervention and prevention methods in advance of ARDS onset to improve patient outcomes. This Small Business Innovation Research (SBIR) Phase I project will use semi-supervised machine learning (SSL) to develop and validate an ARDS prediction screening tool. The goals and anticipated technical results are as follows: Aim 1 will employ semi-supervised deep learning to develop a model for the prediction of ARDS up to 48 hours prior to onset. Because SSL will improve generalized performance, the tool can be applied in settings where many clinical features are not available, including a lack of radiographic data. Aim 2 will validate and optimize the semi-supervised model across external datasets. Validation on external datasets will evaluate the algorithm across a variety of hospital-specific measurement frequencies, demographics, and care practices.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.