SCCM Resource Library-Machine Learning Models for Sepsis Prediction

Video Transcription

Video Summary

In this video, the speaker discusses sepsis prediction using machine learning and artificial intelligence methods. They explain that electronic medical records have been the primary source of data for developing sepsis prediction models, but there are emerging alternatives such as bedside point-of-care tools and pathogen-based biomarkers. The speaker highlights a study that used machine learning to detect sepsis and found that earlier interventions based on machine learning alerts improved outcomes. They also mention the use of continuous data from bedside monitoring and sensor-based tools to generate risk profiles. The video concludes by mentioning challenges and the need for translation of these models into prospective implementation.

Asset Subtitle

Sepsis, 2023

Asset Caption

Type: one-hour concurrent | Challenges in Sepsis Prediction and Prognosis (SessionID 1228529)

Meta Tag

Content Type Presentation

Knowledge Area Sepsis

Membership Level Professional

Membership Level Select

Tag Sepsis

Year 2023

Keywords

sepsis prediction

machine learning

artificial intelligence

electronic medical records

bedside point-of-care tools

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