Huachuan Wang

Huachuan Wang, James Ting-Ho Lo

This paper aims to overcome a fundamental problem in the theory and application of deep neural networks (DNNs). We propose a method to solve the local minimum problem in training DNNs directly. Our method is based on the cross-entropy loss criterion's convexification by transforming the cross-entropy loss into a risk averting error (RAE) criterion. To alleviate numerical difficulties, a normalized RAE (NRAE) is employed. The convexity region of the cross-entropy loss expands as its risk sensitivity index (RSI) increases. Making the best use of the convexity region, our method starts training with an extensive RSI, gradually reduces it, and switches to the RAE as soon as the RAE is numerically feasible. After training converges, the resultant deep learning machine is expected to be inside the attraction basin of a global minimum of the cross-entropy loss. Numerical results are provided to show the effectiveness of the proposed method.

Huachuan Wang, Yuanfei Bi

Mortality prediction in intensive care units is considered one of the critical steps for efficiently treating patients in serious condition. As a result, various prediction models have been developed to address this problem based on modern electronic healthcare records. However, it becomes increasingly challenging to model such tasks as time series variables because some laboratory test results such as heart rate and blood pressure are sampled with inconsistent time frequencies. In this paper, we propose several deep learning models using the same features as the SAPS II score. To derive insight into the proposed model performance. Several experiments have been conducted based on the well known clinical dataset Medical Information Mart for Intensive Care III. The prediction results demonstrate the proposed model's capability in terms of precision, recall, F1 score, and area under the receiver operating characteristic curve.