Clinical deep model to analyse medical multivariate time-series data for health diagnosis

Clinical auxiliary decision-making is related to life and health of patients, so the deep model needs to extract the personalised representation of patients to ensure high analysis and prediction accuracy; and provide a basis for prediction conclusions. In this context, a clinical deep model proposed an interpretable assessment method of patient health status based on contextual learning of medical features, encoding the time-series features of each variable separately, and using a multi-head de-coordination self-attention mechanism for learning Relationships between different features; feature skip-connection encoding based on a compressed excitation mechanism is proposed to improve the sensitivity of the model.