Evaluating the effectiveness of a sliding window technique in machine learning models for mortality prediction in ICU cardiac arrest patients.

Lihi Danay¹, Roni Ramon-Gonen², Maria Gorodetski³, David G Schwartz¹

¹The Graduate School of Business Administration, Bar-Ilan University, Ramat-Gan, Israel.

²The Graduate School of Business Administration, Bar-Ilan University, Ramat-Gan, Israel. Electronic address: Roni.ramon-gonen@biu.ac.il.

³DayTwo LTD, Tel Aviv, Israel.

PubMed DOI

Abstract

Extensive research has been devoted to predicting ICU mortality, to assist clinical teams managing critical patients. Electronic health records (EHR) contain both static and dynamic medical data, with the latter accumulating during ICU stays. Existing models often rely on a fixed time window (e.g., first 24 h) for prediction, potentially missing vital post-24-hour data. The present study aims to i…

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