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Video Transcription
Video Summary
In this video transcript, Adam Disorni from the University of Rochester discusses the use of data science in pediatric critical care. He explains his methodology, which involved reviewing articles from various journals and categorizing them based on topics such as predictive analytics, machine learning, and artificial intelligence. He highlights several articles, including educational articles on machine learning techniques and the use of clinical decision support tools. He also discusses studies on big data analysis, predictive models, and machine learning, as well as informatics and decision support systems. One study focuses on predicting potential transfer to the pediatric intensive care unit, while another looks at predicting recovery from multiple organ dysfunction syndrome. Disorni also mentions a scoping review on early detection of sepsis and a study on differentiating children with sepsis and acute respiratory distress syndrome using proteomics and machine learning. He stresses the importance of external validation and implementation of decision support systems in clinical practice.
Asset Subtitle
Research, Quality and Patient Safety, 2023
Asset Caption
Type: year in review | Year in Review: Pediatrics (SessionID 2000008)
Meta Tag
Content Type
Presentation
Knowledge Area
Research
Knowledge Area
Quality and Patient Safety
Membership Level
Professional
Membership Level
Select
Tag
Clinical Research Design
Tag
Evidence Based Medicine
Year
2023
Keywords
data science
pediatric critical care
predictive analytics
machine learning
artificial intelligence
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