Clinical applications of machine learning in heart failure

Machine learning is increasingly applied to problems in clinical medicine. Heart failure (HF) is a heterogeneous clinical syndrome whereby reductions in cardiac output lead to inadequate blood supply to meet the needs of peripheral tissues. It is an important clinical condition, requiring detailed investigations to guide its diagnosis, phenotyping, prognosis, and clinical management. Machine learning, in particular neural network-based algorithms, can be efficiently and more accurately used for performing all of these tasks by learning from the data. This chapter reviews the latest evidence on the different machine learning methodologies for improving diagnostic accuracy and risk stratification in HF. Despite the advantages of neural network modeling, the disadvantages include difficulty in interpretation with potential limited clinical transferability and applicability. Researchers have addressed these areas of weakness through techniques such as attention-weighting and feature transformation to increase the interpretability of findings from the “black box.” In the future, studies w to improve the predictive performance for HF-related adverse events, such as hospital readmissions, are needed to evaluate better patients’ quality of life and the disease burden on the public health system.