The rate of publication of studies using machine learning to analyze routinely collected ICU data is increasing rapidly. The sample sizes used in many published studies are too small to exploit the potential of these methods.

Methodological and reporting guidelines are needed, particularly with regard to the choice of method and validation of predictions, to increase confidence in reported findings and aid in translating findings towards routine use in clinical practice.

Of 2450 papers identified, 258 fulfilled eligibility criteria.

The most common study aims were predicting complications, predicting mortality, improving prognostic models, and classifying sub-populations.

Median sample size was 488: 41 studies analyzed data on > 10,000 patients.

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