SCCM Resource Library-Identifying Biomarker-Based Pediatric ARDS Subphenotypes Using Machine Learning and Clinical Data

Identifying Biomarker-Based Pediatric ARDS Subphenotypes Using Machine Learning and Clinical Data

Video Transcription

Video Summary

In this video, Dan Balcarcel discusses identifying biomarker-based pediatric ARDS subphenotypes using machine learning and clinical data. ARDS is a common condition in pediatric intensive care units with a high mortality rate and no specific pharmacotherapies. Subtyping ARDS could help develop more precise management strategies and aid in trials. Previous subtyping strategies using inflammatory cytokines have shown promise but require time-consuming and expensive testing. Balcarcel discusses a potential solution using a machine learning classifier model that can predict ARDS subphenotype using readily available clinical data alone. Their model achieved an area under the curve of 0.91, suggesting that subphenotyping children with ARDS could be done quicker and at a lower cost.

Asset Subtitle

Pulmonary, Pediatrics, 2023

Asset Caption

Type: star research | Star Research Presentations: Biomarkers I, Pediatrics (SessionID 30007)

Meta Tag

Content Type Presentation

Knowledge Area Pulmonary

Knowledge Area Pediatrics

Membership Level Professional

Membership Level Select

Tag Acute Respiratory Distress Syndrome ARDS

Tag Pediatrics

Year 2023

Keywords

biomarker-based pediatric ARDS subphenotypes

machine learning

clinical data

ARDS management strategies

machine learning classifier model

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