Objectives: Sepsis is caused by infection and subsequent overreaction of immune system and will severely threaten human life. The early prediction is important for the treatment of sepsis. This report aims to develop an early prediction method for sepsis 6 hours ahead on the basis of clinical electronic health records. Data Sources: Challenge data are released by PhysioNet/Computing in Cardiology Challenge 2019 and obtained from ICU patients in three separate hospital systems. Part of the data from two datasets, including 40,336 subjects, are publicly available, and the remaining are used as hidden test set. A normalized utility score defined by the organizing committee is used for model performance evaluation. Study Selection: The supervised machine learning is applied to tackle this challenge. Specifically, we establish the prediction model under the framework of ensemble learning by integrating the artificial features based on clinical prior knowledge of sepsis with deep features automatically extracted by long short-term memory neural network. Data Extraction: Forty clinical variables, including eight vital signs, 26 laboratory values, and six demographics, were measured and recorded once an hour for each individual, and the binary label (0 or 1) was simultaneously provided for each item. Data Synthesis: The proposed model was evaluated by 30-fold cross-validation. The sensitivity, specificity, and normalized utility score were 0.641 ± 0.022, 0.844 ± 0.007, and 0.401 ± 0.019 on publicly available datasets, respectively. The final normalized utility score our team (UCAS_DataMiner) has obtained was 0.313 on full hidden test set (0.406, 0.373, and –0.215 on test set A, B, and C, respectively). Conclusions: We realized a 6-hour ahead early-onset prediction of sepsis on the basis of clinical electronic health record by ensemble learning. The results indicated the proposed model functioned well in the early prediction of sepsis. In particular, ensemble learning had a significant (p < 0.01) improvement than any single model in performance.

Aspect-Based Multimodal Sentiment Analysis (ABMSA) aims to infer the users’ sentiment polarities over individual aspects using visual, textual, and acoustic signals. Although psychological studies have shown that personality has a direct impact on people's sentiment orientations, most existing methods disregard the potential personality character while executing ABMSA tasks. To tackle this issue, a novel psychological perspective, the people's personalities are introduced. To the best of our knowledge, this paper is the very first study in this field. Different from current pipelined multi-task sentiment analysis methods, an end-to-end ABMSA method called Personality-coupled mUlti-task leaRning framEwork (PURE) is proposed, which strongly couples personality mining and ABMSA tasks in a unified architecture to avoid error propagation and enhance the overall system robustness. Specifically, an adaptive personality feature extraction method is designed to accurately model the first impression of different people's personalities. Then, a multi-task ABMSA framework is designed to strongly couple the multimodal features of aspects extracted by the interactive attention fusion network with people's personalities. Subsequently, the proposed framework optimizes them parallel via extended Bayesian meta-learning. Finally, compared to the current optimal model, the classification accuracy and macro F1 score of the proposed model have both shown significant improvements on public datasets. In addition, PURE is transferable and can effectively couple personality modeling tasks with any other sentiment analysis methods.

Targeted sentiment analysis aims to identify the sentiment polarity of specific target mentions in a sentence. Existing methods employ neural networks to extract the relations between target mentions and their contexts. Recent approaches based on graph convolutional networks can model the syntactic relations extracted by an external parser into adjacency matrices. However, online reviews are informal and complex, the syntactic structures provided by the parser can be incorrect in these syntax-insensitive scenarios. To remedy this defect, we design a novel integrated syntactic and semantic tree (IS $ ^{2}$ tree) by labeling semantic relations between the target mention and contexts in a syntactic dependency tree. Furthermore, a dual-channel graph convolutional network (DCGCN) is proposed to encode the contextual information associated with the target mention by dynamic semantic pruning mechanisms and to also retain the syntactic relations. Experimental results demonstrate that the IS $ ^{2}$ tree has a favorable generalization capability comparing to the state-of-the-art baselines on four public datasets.

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