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Ruoting Li

Showing results (1-10 of 9) with videos related to

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Artificial Intelligence in Medicine|October 7, 2022
Septic shock prediction and knowledge discovery through temporal pattern miningJoseph K Agor, Ruoting Li, Osman Y Özaltın
Frontiers in Artificial Intelligence|August 29, 2025
Improving deceased donor kidney utilization: predicting risk of nonuse with interpretable modelsRuoting Li, Sait Tunç, Osman Y Özaltın, et al.
Medrxiv : the Preprint Server for Health Sciences|August 13, 2025
Automated and interoperable methods for generalizable development of clinical machine-learning models for predicting neuromorbidity in critically ill childrenRuoting Li, Christopher M Horvat, Mehdi Nourelahi, et al.
Medrxiv : the Preprint Server for Health Sciences|February 27, 2026
Leveraging Expert Knowledge and Causal Structure Learning to Build Parsimonious Models of Acute Brain Dysfunction in the Pediatric Intensive Care Unit (PICU)Eddie Pérez Claudio, Christopher M Horvat, W Michael Taylor, et al.
Medrxiv : the Preprint Server for Health Sciences|October 7, 2024
Development, External Validation, and Biomolecular Corroboration of Interoperable Models for Identifying Critically Ill Children at Risk of Neurologic MorbidityChristopher M Horvat, Amie J Barda, Eddie Perez Claudio, et al.
JAMA Network Open|February 4, 2025
Interoperable Models for Identifying Critically Ill Children at Risk of Neurologic MorbidityChristopher M Horvat, Amie J Barda, Eddie Perez Claudio, et al.
Critical Care Medicine|June 12, 2025
Early Use of a Risk-Adjusted Mechanical Ventilation Digital Quality Measure Bundle in a Large Health SystemChristopher M Horvat, Jesse Klug, Ruoting Li, et al.
PLOS Digital Health|July 11, 2025
Raising awareness of potential biases in medical machine learning: Experience from a DatathonHarry Hochheiser, Jesse Klug, Thomas Mathie, et al.
Medrxiv : the Preprint Server for Health Sciences|November 6, 2024
Raising awareness of potential biases in medical machine learning: Experience from a DatathonHarry Hochheiser, Jesse Klug, Thomas Mathie, et al.
Pageof 1

Showing results (1-10 of 9) with videos related to

Sort By:
Pageof 1
Artificial Intelligence in Medicine|October 7, 2022
Septic shock prediction and knowledge discovery through temporal pattern miningJoseph K Agor, Ruoting Li, Osman Y Özaltın
Frontiers in Artificial Intelligence|August 29, 2025
Improving deceased donor kidney utilization: predicting risk of nonuse with interpretable modelsRuoting Li, Sait Tunç, Osman Y Özaltın, et al.
Medrxiv : the Preprint Server for Health Sciences|August 13, 2025
Automated and interoperable methods for generalizable development of clinical machine-learning models for predicting neuromorbidity in critically ill childrenRuoting Li, Christopher M Horvat, Mehdi Nourelahi, et al.
Medrxiv : the Preprint Server for Health Sciences|February 27, 2026
Leveraging Expert Knowledge and Causal Structure Learning to Build Parsimonious Models of Acute Brain Dysfunction in the Pediatric Intensive Care Unit (PICU)Eddie Pérez Claudio, Christopher M Horvat, W Michael Taylor, et al.
Medrxiv : the Preprint Server for Health Sciences|October 7, 2024
Development, External Validation, and Biomolecular Corroboration of Interoperable Models for Identifying Critically Ill Children at Risk of Neurologic MorbidityChristopher M Horvat, Amie J Barda, Eddie Perez Claudio, et al.
JAMA Network Open|February 4, 2025
Interoperable Models for Identifying Critically Ill Children at Risk of Neurologic MorbidityChristopher M Horvat, Amie J Barda, Eddie Perez Claudio, et al.
Critical Care Medicine|June 12, 2025
Early Use of a Risk-Adjusted Mechanical Ventilation Digital Quality Measure Bundle in a Large Health SystemChristopher M Horvat, Jesse Klug, Ruoting Li, et al.
PLOS Digital Health|July 11, 2025
Raising awareness of potential biases in medical machine learning: Experience from a DatathonHarry Hochheiser, Jesse Klug, Thomas Mathie, et al.
Medrxiv : the Preprint Server for Health Sciences|November 6, 2024
Raising awareness of potential biases in medical machine learning: Experience from a DatathonHarry Hochheiser, Jesse Klug, Thomas Mathie, et al.
Pageof 1