Year: 2025
Type: article
Source: Intensive Care Medicine
Authors: Juan C. Rojas, Patrick G. Lyons, Kaveri Chhikara, Vaishvik Chaudhari, Sivasubramanium V. Bhavani, Muna Nour, Kevin G. Buell, Kevin D. Smith, Catherine A. Gao, Saki Amagai, Chengsheng Mao, Yuan Luo, Anna K. Barker, Mark Nuppnau, Michael Hermsen, Jay L. Koyner, Haley Beck, Rachel Baccile, Zewei Liao, Kyle A. Carey, Brenna Park-Egan, Xuan Han, Alexander C. Ortiz, Benjamin E. Schmid, Gary E. Weissman, Chad H. Hochberg, Nicholas E. Ingraham, William F. Parker
Institutions: Rush University, Oregon Health & Science University, University of Chicago, Emory University, Northwestern University, University of Michigan, Michigan Medicine, University of Wisconsin–Madison, University of Chicago Medical Center, Tufts University, University of Pennsylvania, Johns Hopkins University, University of Minnesota
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funders: American Thoracic Society, National Institutes of Health, National Heart, Lung, and Blood Institute, National Institute on Drug Abuse, National Cancer Institute, National Institute of General Medical Sciences, National Institute of Allergy and Infectious Diseases, National Institute of Diabetes and Digestive and Kidney Diseases, U.S. National Library of Medicine, National Center for Advancing Translational Sciences
Awards: Mending a Broken Heart Allocation System with Machine Learning, G4922938341, CRITICAL: Collaborative Resource for Intensive care Translational science, Informatics, Comprehensive Analytics, and Learning, Modeling the Incompleteness and Biases of Health Data, G6844960556, Data Driven Strategies for Substance Misuse Identification in Hospitalized Patients, Using Machine Learning for Early Recognition and Personalized Treatment of Acute Kidney Injury, Improving the efficiency and equity of critical care allocation during a crisis with place-based disadvantage indices, Machine learning to predict cure in severe pneumonia episodes, Research training in respiratory biology, Identifying Areas to Improve ICU Outcomes through Provider Variation, SPOT-IT: Sepsis Prediction in Oncology Through Implementation Science and Technology, Successful Clinical Response In Pneumonia Therapy (SCRIPT) Systems Biology Center, Improving sepsis care with AI-based clinical decision support, Cardiology and Pulmonary Clinical Research Training Program, Sustainable Implementation of Prone Positioning for the Acute Respiratory Distress Syndrome, Identification of Precision Sepsis Subphenotypes Using Vital Sign Trajectories
OpenAlex ID: W4408405652