Characterising risk of in-hospital mortality following cardiac arrest using machine learning: A retrospective international registry study

PLoS Medicine Dec 06, 2018

Nanayakkara S, et al. - Using in-hospital data available within the first 24 hours of admission following cardiac arrest, researchers developed more accurate risk-prediction models with a combination of demographic, physiological, and biochemical information utilizing both logistic regression (LR) and machine learning (ML) techniques. For this investigation, patient-level data were obtained from the Australian and New Zealand Intensive Care Society (ANZICS) Adult Patient Database for patients...

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