Reviewed by Tom, Sanome’s Data Science Team.

This is an outstanding paper on a project to build an EWS to predict circulatory failure. Two things that stood out were the clear combination of clinical and medical expertise – too often one of these is missing in a project – and the creative use of feature engineering to create high-resolution lookback windows.

A clear oversight, however, is the failure to discuss the implications of throwing out over 1/3 of the dataset due to missing lactate and MAP measurements. Patients without these measurements would almost certainly not be in circulatory failure (according to a consultant cardiologist we spoke to!), making it likely that 1/3 of the healthy cohort has been thrown out. Doing this boosts the PPV of your model significantly, meaning there are serious implications for the claimed effectiveness of the model.

Click here to read the paper on

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