Using Implementation Science to Assess Barriers to Agreement on Sedation Goal Setting and Assessment.

Objectives: Sedation assessment and goal setting using a validated assessment tool are key components of the ICU Liberation bundle. Appropriate integration of these bundle elements into daily practice remains challenging. Understanding barriers is an important step toward implementation of these best practice bundle elements.

Multisource representation learning for pediatric knowledge extraction from electronic health records.

Electronic Health Record (EHR) systems are particularly valuable in pediatrics due to high barriers in clinical studies, but pediatric EHR data often suffer from low content density. Existing EHR code embeddings tailored for the general patient population fail to address the unique needs of pediatric patients. To bridge this gap, we introduce a transfer learning approach, MUltisource Graph Synthesis (MUGS), aimed at accurate knowledge extraction and relation detection in pediatric contexts.

Electrocardiogram-Based Deep Learning to Predict Mortality in Repaired Tetralogy of Fallot.

Background: Artificial intelligence-enhanced electrocardiogram (AI-ECG) analysis shows promise to predict mortality in adults with acquired cardiovascular diseases. However, its application to the growing repaired tetralogy of Fallot (rTOF) population remains unexplored.

Objectives: This study aimed to develop and externally validate an AI-ECG model to predict 5-year mortality in rTOF.