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

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. The predictive ability of any model is usually expressed as a c-statistic, referring to the area under a receiver operating characteristic curve (AUC), where a value of 1 denotes perfect predictive performance while a value of 0.5 denotes the performance of chance, such as with the flipping of a coin. To date, over 50 different models have been designed to predict HF mortality, incorporating as few as 5 and up to as many as 300 variables.3 These have reported moderate to good performance AUC values ranging from 0.6 to 0.8. Prediction of outcomes other than mortality has proven more challenging, with only fair to moderate predictive capability (0.55–0.7). These limitations have led to significant inertia in uptake of the use of predictive algorithms and to the study of alternative analytic techniques. Machine learning (ML), or the use of computer algorithms that allow for modification to identify complex patterns from large datasets, is a set of new techniques that are often categorized as artificial intelligence (AI).4 Application of these technologies has proliferated and evolved quickly across numerous fields of clinical care. HF is a complex and dynamic disorder with numerous multi-dimensional interactions and is associated with a high clinical and economic burden. In this context, risk prediction for HF outcomes would seem a particularly good fit for ML applications. However, early attempts to use ML-based prediction of HF outcomes