Intensive Care Unit
Intensive care units (ICUs) provide critical care for severely ill patients, and research focuses on improving patient outcomes through advanced monitoring and prediction of adverse events. Current research employs machine learning, particularly deep learning models like transformers, recurrent neural networks (RNNs), and convolutional neural networks (CNNs), often incorporating multimodal data (vital signs, clinical notes, images, audio) to predict mortality, organ failure, and the need for interventions like mechanical ventilation. These advancements aim to enhance the efficiency and effectiveness of ICU care, enabling earlier interventions and improved resource allocation, ultimately leading to better patient outcomes and reduced healthcare costs.
Papers
Predicting risk of delirium from ambient noise and light information in the ICU
Sabyasachi Bandyopadhyay, Ahna Cecil, Jessica Sena, Andrea Davidson, Ziyuan Guan, Subhash Nerella, Jiaqing Zhang, Kia Khezeli, Brooke Armfield, Azra Bihorac, Parisa Rashidi
AI-Enhanced Intensive Care Unit: Revolutionizing Patient Care with Pervasive Sensing
Subhash Nerella, Ziyuan Guan, Scott Siegel, Jiaqing Zhang, Ruilin Zhu, Kia Khezeli, Azra Bihorac, Parisa Rashidi