Multi-modality machine learning approach for risk stratification in heart failure with left ventricular ejection fraction ≤ 45.

ABSTRACT: Heart failure (HF) involves complex remodelling leading to electrical and mechanical dysfunction. We hypothesized that machine learning approaches incorporating data obtained from different investigative modalities including atrial and ventricular measurements from electrocardiography and echocardiography, blood inflammatory marker [neutrophil-to-lymphocyte ratio (NLR)], and prognostic nutritional index (PNI) will improve risk stratification for adverse outcomes in HF compared to logistic regression. Consecutive Chinese patients referred to our centre for transthoracic echocardiography and subsequently diagnosed with HF, between 1 January 2010 and 31 December 2016, were included in this study. Two machine learning techniques, multilayer perceptron and multi-task learning, were compared wit

SUBMITTER: Tse G 

PROVIDER: S-EPMC7754744 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature