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
Multi-Task Prediction of Clinical Outcomes in the Intensive Care Unit using Flexible Multimodal Transformers
Benjamin Shickel, Patrick J. Tighe, Azra Bihorac, Parisa Rashidi
Early Prediction of Mortality in Critical Care Setting in Sepsis Patients Using Structured Features and Unstructured Clinical Notes
Jiyoung Shin, Yikuan Li, Yuan Luo