ATS 2024 Final Program

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231

TUESDAY • MAY 21

212 Impact of a Deep Learning-based Sepsis Prediction Model on Quality of Care and Survival: A Causal Impact Analysis Using Before-and-After Observational Data 213 Development of a Machine Learning Model to Predict Serum Creatinine in Critically Ill Adults 214 Correcting Real-World Pulse Oximetry Readings Using Electronic Health Records and Machine Learning 215 A Retrospective Causal Inference-based Study Using Machine Learning for Identifying Treatment Effects of Various Therapies in Sepsis-induced Acute Respiratory Failure Phenotypes 216 Using Machine Learning to Predict the Effective Treatments for Patients With Clinical Deterioration 217 Discordance Between Clinician Actions and Artificial Intelligence Recommendations in the Treatment of Sepsis 218 Performance Evaluation of an Artificial Intelligence (AI)-based Algorithm for Incidental Findings of Pulmonary Embolism 219 A Natural Language Processing Method to Determine Endotracheal Tube Presence in a Public Image Database 220 Radiological Phenotypes of Acute Respiratory Distress Syndrome (ARDS) 221 Generation and Evaluation of Synthetic Critical Care Progress Notes With Large Language Models 222 Advancing Multi-center Clinical Research in Critically Ill Patients Through the Development of a Common Longitudinal ICU Format (CLIF) 223 Systematic Review of Publicly Available Critical Care Databases for Retrospective Statistical and Machine Learning Analysis 224 Automated Electronic Alerts Reduce Hyperoxemia in Mechanically Ventilated Patients

BEHAVIORAL • CLINICAL POSTER DISCUSSION SESSION

C22 ARTIFICIAL INTELLIGENCE IN THE ICU: THE MACHINE WILL SEE YOU NOW 9:15 a.m. - 11:15 a.m. San Diego Convention Center Room 1A-B (Upper Level) Poster Viewing 9:15-10:00 Discussion 10:00-11:15 201 Training and Validation of a Pandemic Clinical Decision Support Model for Ventilators Using Collective Intelligence and Imitation Learning 202 Local Validation of a Machine Learning Model for Mechanical Ventilation 203 Deep Learning-driven Prediction of Future Blood Pressure in the Intensive Care Unit 204 Systematic Evaluation of General Large Language Models for Contextually Assessed Semantic Concepts From Unstructured Critical Care Data 205 Utility of Multimodal Large Language Models in Analyzing Chest X-ray With Incomplete Contextual Information 206 Discriminatory Performance of Commonly Used Risk Triage Tools in Hospitalized Recipients of Hematopoietic Stem Cell Transplantation 207 Natural Language Processing and Machine Learning to Predict High-intensity Care in Patients With Alcohol Withdrawal Syndrome 208 Prediction Model Using Machine Learning for 90-day Mortality of Patients With Sepsis in Intensive Care Unit From MIMIC-IV Dataset 209 Automated Sequential Organ Failure Assessment: A Practical Alternative to Manual Sofa Scoring for Large Scale Data Analysis With Excellent Prognostic Performance 210 Frequency, Duration of, and Risk Factors for Diagnostic Delays Associated With Sepsis 211 Development and External Validation of a Natural Language Processing Tool to Identify Hospitalized Patients With Infection on Admission

ATS 2024 Conference Program • San Diego, CA