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Can temporal abstraction and classification help to evaluate ICU Glucose Management?

This study investigates how temporal abstraction and classification techniques can identify patient attributes that distinguish those who respond well to blood glucose level (BGL) management from those who do not in an ICU setting. Using data from over 700 patients, we analyze both static and temporal information, including physiological data within the first 24 hours and severity of illness scores. The findings will help improve personalized care strategies for glucose management, potentially leading to better outcomes for critically ill patients.

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Can temporal abstraction and classification help to evaluate ICU Glucose Management?

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  1. Can temporal abstraction and classification help to evaluate ICU Glucose Management? Barry Nannings1, Ameen Abu-Hanna1, Robert-Jan Bosman2 1Academic Medical Center, University of Amsterdam, Amsterdam 2Onze Lieve Vrouwe Gasthuis, Amsterdam

  2. Goal Discover patient attributes that distinguish between patients that: • Respond well to BGL management • Don’t respond well to BGL management Are different attributes important within different time frames ?

  3. Data Static data, 100+ attributes (n = 700) • ‘Constant’ attributes • Physiological information first 24 hrs • Severity of illness scores Temporal data, 3 attributes (n = 16000) • Time • BGL • Insulin pump settings

  4. Glucose Time Temporal Abstraction Glucose Time

  5. Classification

  6. Discover relevant features Atribute Atribute Atribute

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