Cautious optimism for machine learning techniques for prediction of heart failure outcomes.

B This article refers to 'A machine learning risk score predicts mortality across the spectrum of left ventricular ejection fraction' by B. Greenberg I et al i ., published in this issue on pages 995-999. b The desire to predict future events has been a basic human quality. It is therefore not surprising that numerous attempts to estimate mortality from heart failure (HF) have been made.1,2 In most cases, a predictive model is constructed by performing one or more regression techniques upon a clinical dataset and validated in a different dataset. In this context, risk prediction for HF outcomes would seem a particularly good fit for ML applications. [Extracted from the article]