Autonomous detection of disruptions in the intensive care unit using deep mask RCNN

Kumar Rohit Malhotra, Anis Davoudi, Scott Siegel, Azra Bihorac, Parisa Rashidi

Abstract

Patients staying in the Intensive Care Unit (ICU) have a severely disrupted circadian rhythm. Due to patients’ critical medical condition, ICU physicians and nurses have to provide round-the-clock clinical care, further disrupting patients’ circadian rhythm. Mistimed family visits during rest-time can also disrupt patients’ circadian rhythm. Currently, such effects are only reported based on hospital visitation policies rather than the actual number of visitors and care providers in the room. To quantify visitation disruptions, we used a deep Mask R-CNN model, a deep learning framework for object instance segmentation to detect and quantify the number of individuals in the ICU unit. This study represents the first effort to automatically quantify visitations in an ICU room, which could have implications in terms of policy adjustment, as well as circadian rhythm investigation. Our model achieved precision of 0.97 and recall of 0.67, with F1 score of 0.79 for detecting disruptions in the ICU units.