Early prediction of sepsis using double fusion of deep features and handcrafted features

Abstract

Sepsis is a life-threatening medical condition that is characterized by the dysregulated immune system response to infections, having both high morbidity and mortality rates. Early prediction of sepsis is critical to the decrease of mortality. This paper presents a novel early warning model called Double Fusion Sepsis Predictor (DFSP) for sepsis onset. DFSP is a double fusion framework that combines the benefits of early and late fusion strategies. First, a hybrid deep learning model that combines both the convolutional and recurrent neural networks to extract deep features is proposed. Second, deep features and handcrafted features, such as clinical scores, are concatenated to build the joint feature representation (early fusion). Third, several tree-based models based on joint feature representation are developed to generate the risk scores of sepsis onset that are combined with an End-to-End neural network for final sepsis detection (late fusion). To evaluate DFSP, a retrospective study was conducted, which included patients admitted to the ICUs of a hospital in Shanghai China. The results demonstrate that the DFSP outperforms state-of-the-art approaches in early sepsis prediction.

Coupling deep and handcrafted features to assess smile genuineness

“…As both handcrafted and deeply-learned features have certain benefits and limitations, there have been many attempts to combine these two kinds of features to further improve the classification capabilities. Such approaches were elaborated for a variety of computer vision tasks, 21,22 including facial expression recognition. 23 However, such approaches have not been studied so far for assessing smile genuineness and to our best knowledge this paper reports the first attempt to fill this gap.…”